Packages and custom functions
The lnRR_func function is here used to calculate a log response ratio (lnRR) adjusted for small sample sizes. In addition, this formula accounts for correlated samples. For more details, see Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644
# packages
library(tidyverse)
library(googlesheets4)
library(here)
library(metafor)
library(metaAidR) # see a note above
library(orchaRd) # see a note above
library(ape)
library(clubSandwich)
library(metaAidR)
library(patchwork)
library(emmeans) # see a note above
library(kableExtra)
library(GGally)
library(cowplot)
library(grDevices) # reqired for using base and ggplots together
# Below is the custom function to calculate the lnRR
lnRR_func <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e, rho = 0.5) {
lnRR <- log(Me/Mc) + 0.5 * ((aCV2e/Ne) - (aCV2c/Nc))
var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne) - 2 * rho * ((aCV2c * aCV2e)/sqrt(Nc * Ne))
data.frame(lnRR, var_lnRR)
}
# Mc: Concentration of PFAS of the raw (control) sample Nc: Sample size of the
# raw (control) sample Me: Concentration of PFAS of the cooked (experimental)
# sample Ne: Sample size of the cooked (experimental) sample aCV2c: Mean
# coefficient of variation of the raw (control) samples aCV2e: Mean coefficient
# of variation of the cooked (experimental) samplesData import and processing
Import and process raw data
Import raw data
raw_data <- read_sheet("https://docs.google.com/spreadsheets/d/1cbmYDfIc2dxHJxBaowojUZZkN31NW4sL_pHw0t9eTTU/edit#gid=477880397",
range = "Data_extraction_2", skip = 1, col_types = "ccncccccncncccccnncccnccnncncnccnnncncncccccccc") # Import raw dataProcess raw data
processed_data <- filter(raw_data, !PFAS_type == "PFOS_Total")
processed_data <- filter(processed_data, !Species_common == "Fish cake")
write.csv(processed_data, here("data", "Rawdata.csv"), row.names = F)Load processed data
processed_data <- read.csv(here("data", "Rawdata.csv"))
dat <- processed_data %>% mutate(SDc = ifelse(Sc_technical_biological == "biological", Sc, NA), # Calculate the SD of biological replicates for control samples
SDe = ifelse(Se_technical_biological == "biological", Se, NA)) # Calculate the SD of biological replicates for experimental samples
#### Ratio_liquid_fish with "0" for the dry cooking category
dat<-dat %>% mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category =="No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is "No liquid", otherwise keep the same value of Ratio_liquid_fish
# arrange(select(dat, Cooking_Category, Ratio_liquid_fish, Ratio_liquid_fish_0), Cooking_Category) # Checking everything is fine
kable(dat, "html") %>% kable_styling("striped", position = "left") %>% scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Ratio_liquid_fish_0 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F001 | Alves_2017 | 2017 | Portugal | E001 | Flounder | Platichthys flesus | vertebrate | marine fish | 7.430000 | PFOS | 8 | linear | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C001 | NA | 25 | 1 | ng/g | 24.0000000 | NA | 1.5280000 | sd | technical | 25 | 1 | 22.0000000 | NA | 1.5300000 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA | NA |
| F001 | Alves_2017 | 2017 | Portugal | E002 | Mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C002 | NA | 25 | 1 | ng/g | 3.1000000 | NA | 0.2120000 | sd | technical | 25 | 1 | 2.9000000 | NA | 0.1410000 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E003 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFUnDA | 11 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 13.3018868 | NA | 0.0471698 | sd | technical | 25 | 1 | 4.1509434 | NA | 0.0943396 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E004 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFDoDA | 12 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 3.5731707 | NA | 0.0243902 | sd | technical | 25 | 1 | 3.2073171 | NA | 0.0243902 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E005 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFTrA | 13 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 6.5283019 | NA | 0.0754717 | sd | technical | 25 | 1 | 10.0377358 | NA | 0.0754717 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E006 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFTA | 14 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 1.3736842 | NA | 0.0157895 | sd | technical | 25 | 1 | 1.3315789 | NA | 0.0210526 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E007 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 0.6467391 | NA | 0.0054348 | sd | technical | 25 | 1 | 0.3016304 | NA | 0.0081522 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E008 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 25 | 1 | 0.0869767 | NA | 0.0130233 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E009 | European plaice | Pleuronectes platessa | vertebrate | marine fish | 8.700000 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C004 | NA | 25 | 1 | ng/g | 0.2472826 | NA | 0.0081522 | sd | technical | 25 | 1 | 0.2527174 | NA | 0.0054348 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E010 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.770000 | PFBA | 3 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | NA | 50 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 50 | 1 | 0.2083333 | NA | 0.0090909 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E011 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.770000 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | NA | 50 | 1 | ng/g | 0.0241860 | NA | 0.0074419 | sd | technical | 50 | 1 | 0.0250000 | <LOQ | NA | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F003 | Bhavsar_2014 | 2014 | Canada | E012 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0860000 | NA | 0.1350000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | ML | 0.0950000 | 0.1350000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E013 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1920000 | NA | 0.2660000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2660000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E014 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2340000 | NA | 0.2910000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.2910000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E015 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.1010000 | NA | 0.0950000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0950000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E016 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2590000 | NA | 0.2410000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2410000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E017 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0730000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0730000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E019 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.5600000 | NA | 18.0000000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 18.0000000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E020 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3970000 | NA | 0.4330000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.4330000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E021 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0020000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E022 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0100000 | NA | 0.0160000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0160000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E023 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.1180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1180000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E024 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1900000 | NA | 0.2320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2320000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E025 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2560000 | NA | 0.3100000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.3100000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E026 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.1000000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0800000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E027 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2850000 | NA | 0.2340000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2340000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E028 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0710000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0710000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E030 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.4500000 | NA | 15.6300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 15.6300000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E031 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3920000 | NA | 0.3590000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.3590000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E032 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0030000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E033 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0140000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0220000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E034 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0780000 | NA | 0.1140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1140000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E035 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1820000 | NA | 0.2220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2220000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E036 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2270000 | NA | 0.2550000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.2550000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E037 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.0960000 | NA | 0.0810000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0810000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E038 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2750000 | NA | 0.2160000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2160000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E039 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0670000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0670000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E041 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.0300000 | NA | 15.1900000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 15.1900000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E042 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3930000 | NA | 0.3690000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.3690000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E043 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0030000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E044 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0130000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0220000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E045 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.0990000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0220000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E046 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5660000 | NA | 0.1380000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1380000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E047 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8040000 | NA | 0.1670000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1670000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E048 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.0960000 | NA | 0.3960000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.3960000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E049 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.7740000 | NA | 0.3320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.3320000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E050 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.1400000 | NA | 0.8740000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.8740000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E051 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.3410000 | NA | 0.3910000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.3910000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E052 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 30.5200000 | NA | 9.2540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 9.2540000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E053 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0840000 | NA | 0.5710000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.5710000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E054 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1050000 | NA | 0.0600000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0600000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E055 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C010 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1800000 | NA | 0.0840000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0840000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E056 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.1050000 | NA | 0.0370000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0370000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E057 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5480000 | NA | 0.1210000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1210000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E058 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8480000 | NA | 0.1550000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1550000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E059 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.1080000 | NA | 0.4040000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.4040000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E060 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.8280000 | NA | 0.4180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.4180000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E061 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.1150000 | NA | 0.7690000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.7690000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E062 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.2910000 | NA | 0.3460000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.3460000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E063 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 28.3700000 | NA | 11.9900000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 11.9900000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E064 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0450000 | NA | 0.6230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.6230000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E065 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1170000 | NA | 0.0730000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0730000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E066 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1900000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0800000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E067 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.1010000 | NA | 0.0350000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0350000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E068 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5690000 | NA | 0.1080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1080000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E069 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8300000 | NA | 0.1300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1300000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E070 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.0440000 | NA | 0.3560000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.3560000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E071 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.7460000 | NA | 0.2830000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.2830000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E072 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.0670000 | NA | 0.7540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.7540000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E073 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.3590000 | NA | 0.4280000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.4280000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E074 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 28.1100000 | NA | 10.9300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 10.9300000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E075 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0900000 | NA | 0.6180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.6180000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E076 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1060000 | NA | 0.0650000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0650000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E077 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1880000 | NA | 0.0750000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0750000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E078 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3700000 | NA | 0.1890000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1890000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E079 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.5100000 | NA | 0.2320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2320000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E080 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.6850000 | NA | 0.2930000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.2930000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E081 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2210000 | NA | 0.1140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1140000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E082 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.4840000 | NA | 0.2640000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2640000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E083 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1370000 | NA | 0.0510000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0510000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E084 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2480000 | NA | 0.0610000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0610000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E085 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 20.5100000 | NA | 6.7520000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 6.7520000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E086 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.4740000 | NA | 0.1960000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.1960000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E087 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0020000 | NA | 0.0020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0020000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E088 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0180000 | NA | 0.0090000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0090000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E089 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3580000 | NA | 0.1700000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1700000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E090 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.5280000 | NA | 0.2330000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2330000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E091 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.7250000 | NA | 0.3450000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.3450000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E092 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2370000 | NA | 0.1110000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1110000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E093 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.5580000 | NA | 0.2800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2800000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E094 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1490000 | NA | 0.0680000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0680000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E095 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2630000 | NA | 0.0870000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0870000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E096 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 22.1100000 | NA | 7.8970000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 7.8970000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E097 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.5600000 | NA | 0.2260000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.2260000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E098 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0120000 | NA | 0.0180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0180000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E099 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0160000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0060000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E100 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3740000 | NA | 0.1810000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1810000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E101 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.4930000 | NA | 0.2070000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2070000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E102 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.6830000 | NA | 0.2860000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.2860000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E103 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2320000 | NA | 0.1030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1030000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E104 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.5190000 | NA | 0.2120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2120000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E105 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1290000 | NA | 0.0450000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0450000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E106 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2450000 | NA | 0.0770000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0770000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E107 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 21.6700000 | NA | 8.0080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 8.0080000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E108 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.5160000 | NA | 0.2440000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.2440000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E109 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0020000 | NA | 0.0010000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0010000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E110 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0160000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0060000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E111 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0790000 | NA | 0.0230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0230000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E112 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3490000 | NA | 0.0940000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0940000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E113 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3330000 | NA | 0.0910000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0910000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E114 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1330000 | NA | 0.0120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0120000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E115 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.1800000 | NA | 0.0210000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0210000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E116 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0930000 | NA | 0.0230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0230000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E117 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0980000 | NA | 0.0340000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0340000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E118 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 45.0900000 | NA | 3.7090000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 3.7090000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E119 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1780000 | NA | 0.0940000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0940000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E120 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0350000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0060000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E121 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0630000 | NA | 0.0170000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0170000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E122 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0740000 | NA | 0.0140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0140000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E123 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3380000 | NA | 0.0980000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0980000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E124 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3480000 | NA | 0.1020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.1020000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E125 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1440000 | NA | 0.0370000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0370000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E126 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.2170000 | NA | 0.0410000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0410000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E127 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0940000 | NA | 0.0250000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0250000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E128 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0880000 | NA | 0.0360000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0360000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E129 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 52.6900000 | NA | 14.6200000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 14.6200000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E130 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1890000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0800000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E131 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0400000 | NA | 0.0080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0080000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E132 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0120000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E133 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0670000 | NA | 0.0150000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0150000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E134 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.2990000 | NA | 0.0720000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0720000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E135 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3070000 | NA | 0.0760000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0760000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E136 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1290000 | NA | 0.0490000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0490000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E137 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.1790000 | NA | 0.0540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0540000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E138 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0340000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0340000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E139 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0270000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0270000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E140 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 44.5100000 | NA | 7.7180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 7.7180000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E141 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1570000 | NA | 0.0660000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0660000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E142 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0290000 | NA | 0.0040000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0040000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E143 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0770000 | NA | 0.0050000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0050000 | 0.0615832 |
| F005 | DelGobbo_2008 | 2008 | Canada | E144 | Catfish | Ictalurus punctatus | vertebrate | freshwater fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C017 | NA | 19 | 1 | ng/g | 1.5657252 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.8987374 | NA | NA | technical | 4 | ng/g | 0.3646058391 | 1.093817517 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | ML | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E145 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | NA | 14 | 1 | ng/g | 1.3600000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0169896 | LOD | NA | technical | 4 | ng/g | 0.01698962618 | 0.05096887855 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E146 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | NA | 14 | 1 | ng/g | 0.3715856 | LOD | NA | Not available because sample size is one. | technical | 14 | 1 | 0.4700000 | NA | NA | technical | 4 | ng/g | 0.3715856481 | 1.114756944 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E147 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 0.0774969 | LOD | NA | Not available because sample size is one. | technical | 9 | 1 | 0.0600000 | NA | NA | technical | 4 | ng/g | 0.07749693852 | 0.2324908155 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E148 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9 | 1 | 0.0032120 | LOD | NA | technical | 4 | ng/g | 0.0032120281 | 0.009636084301 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E149 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 0.0270203 | LOD | NA | Not available because sample size is one. | technical | 9 | 1 | 0.3900000 | NA | NA | technical | 4 | ng/g | 0.02702032357 | 0.08106097072 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E150 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9 | 1 | 0.2200000 | NA | NA | technical | 4 | ng/g | 0.2333732266 | 0.7001196799 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E151 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 0.7800000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0600000 | NA | NA | technical | 3 | ng/g | 0.02612585327 | 0.0783775598 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E152 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.2900000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0261259 | LOD | NA | technical | 3 | ng/g | 0.02612585327 | 0.0783775598 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E153 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFDA | 10 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0120876 | LOD | NA | technical | 3 | ng/g | 0.01208759187 | 0.03626277562 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E154 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.8800000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 1.5900000 | NA | NA | technical | 3 | ng/g | 0.02340346342 | 0.07021039026 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E155 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 2.6100000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0071943 | LOD | NA | technical | 3 | ng/g | 0.007194278092 | 0.02158283428 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E156 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 0.5086163 | LOD | NA | Not available because sample size is one. | technical | 15 | 1 | 0.2300000 | NA | NA | technical | 3 | ng/g | 0.5086163051 | 1.525848915 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E157 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C021 | NA | 19 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.2100000 | NA | NA | technical | 4 | ng/g | 0.335745729 | 1.007237187 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E158 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C021 | NA | 19 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.7800000 | NA | NA | technical | 4 | ng/g | 0.2127077334 | 0.6381232001 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E159 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | NA | 22 | 1 | ng/g | 1.5800000 | NA | NA | Not available because sample size is one. | technical | 22 | 1 | 1.5900000 | NA | NA | technical | 3 | ng/g | 0.03079926295 | 0.09239778884 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E160 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | NA | 22 | 1 | ng/g | 1.3200000 | NA | NA | Not available because sample size is one. | technical | 22 | 1 | 0.9600000 | NA | NA | technical | 3 | ng/g | 0.004661629686 | 0.01398488906 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E161 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.0900000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0027709 | LOD | NA | technical | 4 | ng/g | 0.002770915071 | 0.008312745212 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E162 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 1.3500000 | NA | NA | technical | 4 | ng/g | 0.01203365344 | 0.03610096033 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E163 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFDoDA | 12 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.3300000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0255728 | LOD | NA | technical | 4 | ng/g | 0.02557281543 | 0.07671844628 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E164 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 0.6700000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0070174 | LOD | NA | technical | 4 | ng/g | 0.007017439682 | 0.02105231905 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E165 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.5100000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.8800000 | NA | NA | technical | 4 | ng/g | 0.3642166626 | 1.092649988 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E166 | Yellow croaker | Larimichthys polyactis | vertebrate | tunicata | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | NA | 35 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.0179042 | LOD | NA | technical | 4 | ng/g | 0.0179042065 | 0.0537126195 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E167 | Yellow croaker | Larimichthys polyactis | vertebrate | tunicata | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | NA | 35 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.8900000 | NA | NA | technical | 4 | ng/g | 0.3768854178 | 1.130656253 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E168 | Yellow croaker | Larimichthys polyactis | vertebrate | tunicata | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | NA | 35 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 2.1100000 | NA | NA | technical | 4 | ng/g | 0.0165860278 | 0.04975808341 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E169 | Yellow croaker | Larimichthys polyactis | vertebrate | tunicata | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | NA | 35 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.6800000 | NA | NA | technical | 4 | ng/g | 0.3921755285 | 1.176526586 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F006 | Hu_2020 | 2020 | China | E170 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFBA | 3 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 5.3412073 | NA | 1.6889253 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | ML | 7.4193907 | 1.6889253 | NA |
| F006 | Hu_2020 | 2020 | China | E171 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFOA | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.2674068 | NA | 0.0800584 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0800584 | NA |
| F006 | Hu_2020 | 2020 | China | E172 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFBS | 4 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 23.9801208 | NA | 26.8453690 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 26.8453690 | NA |
| F006 | Hu_2020 | 2020 | China | E173 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFOS | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 122.4133110 | NA | 62.4690572 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 62.4690572 | NA |
| F006 | Hu_2020 | 2020 | China | E174 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFHpA | 7 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 55.3995680 | NA | 55.3995680 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 55.3995680 | NA |
| F006 | Hu_2020 | 2020 | China | E175 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFDoDA | 12 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.2676991 | NA | 1.5334164 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 1.5334164 | NA |
| F006 | Hu_2020 | 2020 | China | E176 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFHxS | 6 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.8685897 | NA | 0.3034431 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.3034431 | NA |
| F006 | Hu_2020 | 2020 | China | E177 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | FOSA | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.3838798 | NA | 1.2904183 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 1.2904183 | NA |
| F006 | Hu_2020 | 2020 | China | E178 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFBA | 3 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 4.9146982 | NA | 7.4344664 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 7.4344664 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E179 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.1932566 | NA | 0.0707998 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0707998 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E180 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFBS | 4 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 10.8230680 | NA | 7.4606797 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 7.4606797 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E181 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFOS | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 97.7348993 | NA | 23.1725546 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 23.1725546 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E182 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFHpA | 7 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 13.7149028 | NA | 23.6036055 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 23.6036055 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E183 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFDoDA | 12 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.3534292 | NA | 2.4839931 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 2.4839931 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E184 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFHxS | 6 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.6506410 | NA | 0.1079317 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.1079317 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E185 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | FOSA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.2540984 | NA | 1.2484167 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 1.2484167 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E186 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFBA | 3 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 7.9068241 | NA | 9.3812679 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 9.3812679 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E187 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFOA | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.2308114 | NA | 0.1541468 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.1541468 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E188 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFBS | 4 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 9.8657220 | NA | 5.8014926 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 5.8014926 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E189 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFOS | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 134.4379195 | NA | 58.0538019 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 58.0538019 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E190 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFHpA | 7 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 23.7041037 | NA | 35.9297367 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 35.9297367 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E191 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFDoDA | 12 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.8733407 | NA | 2.7470061 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 2.7470061 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E192 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFHxS | 6 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 1.1602564 | NA | 0.7375647 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.7375647 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E193 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | FOSA | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 3.7500000 | NA | 3.7411362 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 3.7411362 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E194 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFBA | 3 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 4.8490814 | NA | 6.9303363 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 6.9303363 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E195 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFOA | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.1652961 | NA | 0.0630496 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0630496 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E196 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFBS | 4 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 7.5376305 | NA | 1.5022632 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 1.5022632 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E197 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFOS | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 121.7142058 | NA | 62.5574247 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 62.5574247 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E198 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFHpA | 7 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 10.0971922 | NA | 16.4902451 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 16.4902451 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E199 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFDoDA | 12 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.9120575 | NA | 3.3602781 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 3.3602781 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E200 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFHxS | 6 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.8253205 | NA | 0.2542197 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.2542197 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E201 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | FOSA | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.2814208 | NA | 0.4304018 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 0.4304018 | 0.1428571 |
| F007 | Kim_2020 | 2020 | Korea | E202 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. For volume of cooking liquid: 1 cup is 250 ml, accordingly for table spoon etc. | ML | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E203 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1100000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E204 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | NA | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E205 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0600000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E206 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E207 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1300000 | NA | 0.0400000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E208 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1400000 | NA | 0.0100000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E209 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E210 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E211 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E212 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E213 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E214 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E215 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E216 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E217 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E218 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0500000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E219 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0200000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E220 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E221 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E222 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E223 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E224 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E225 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E316 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 20.7900000 | NA | 0.1700000 | sd | technical | 5 | 1 | 16.7700000 | NA | 0.4200000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | Scientific name of swimming crab not provided in paper, inferred as this species of swimming crab is commonly eaten in South korea (Kim, S., Lee, M.J., Lee, J.J., Choi, S.H. and Kim, B.S., 2017. Analysis of microbiota of the swimming crab (Portunus trituberculatus) in South Korea to identify risk markers for foodborne illness. LWT, 86, pp.483-491.) | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E317 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOS | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.8100000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.7400000 | NA | 0.0300000 | technical | NA | ng/g | 0.07 | 0.07 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E318 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFBA | 3 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.1400000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.0400000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E319 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHpA | 7 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.3700000 | NA | 0.0300000 | sd | technical | 5 | 1 | 0.3200000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.17 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E320 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 2.8900000 | NA | 0.0200000 | sd | technical | 5 | 1 | 2.3000000 | NA | 0.0300000 | technical | NA | ng/g | 0.03 | 0.08 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E321 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.6600000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.5700000 | NA | 0.0200000 | technical | NA | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E322 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.9300000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.7900000 | NA | 0.0200000 | technical | NA | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E323 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.2500000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.2300000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E324 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTrA | 13 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 1.1200000 | NA | 0.0600000 | sd | technical | 5 | 1 | 1.3800000 | NA | 0.0900000 | technical | NA | ng/g | 0.05 | 0.16 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E325 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTA | 14 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.2800000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.2600000 | NA | 0.0200000 | technical | NA | ng/g | 0.05 | 0.15 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E326 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHxS | 6 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.4800000 | NA | 0.0300000 | sd | technical | 5 | 1 | 0.3300000 | NA | 0.0300000 | technical | NA | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E327 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDS | 10 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.0400000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.0400000 | NA | 0.0100000 | technical | NA | ng/g | 0.09 | 0.27 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E328 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | FOSA | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 1.5400000 | NA | 0.0900000 | sd | technical | 5 | 1 | 2.5500000 | NA | 0.1900000 | technical | NA | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E329 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1590000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | ML - note shared controls for differend cooking times and methods | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E330 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1170000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E331 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0790000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E332 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E333 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1160000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E334 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E335 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1400000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E336 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1330000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E337 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0710000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E338 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2010000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E339 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0590000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E340 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E341 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 14.7000000 | NA | 0.0090000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E342 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 9.3500000 | NA | 0.0080000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E343 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.6600000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E344 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 5.6300000 | NA | 0.0050000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E345 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 4.5000000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E346 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.7700000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E347 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 8.2800000 | NA | 0.0070000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E348 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 6.6200000 | NA | 0.0060000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E349 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.4800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E350 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 4.4900000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E351 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.0500000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E352 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 2.8300000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E353 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E354 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E355 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E356 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2030000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E357 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1390000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E358 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1040000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E359 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E360 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E361 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E362 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E363 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0510000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E364 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2550000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E365 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 4.7800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E366 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 3.5000000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E367 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.5100000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E368 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 7.0500000 | NA | 0.0060000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E369 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.4700000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E370 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.7600000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E371 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 3.0300000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E372 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.0400000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E373 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.2300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E374 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 4.2800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E375 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.7800000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E376 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.0200000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E377 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E378 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1870000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E379 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E380 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1750000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E381 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1530000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E382 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E383 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E384 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1320000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E385 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0930000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E386 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1810000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E387 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0880000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E388 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0660000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E389 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.1500000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E390 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 2.6500000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E391 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.2300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E392 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.4400000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E393 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 2.3600000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E394 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.6500000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E395 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 3.6800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E396 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.7300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E397 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 0.9200000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E398 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.0300000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E399 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.9700000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E400 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 0.8400000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E401 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E402 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E403 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E404 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E405 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1210000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E406 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E407 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1680000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E408 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1340000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E409 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0910000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E410 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1740000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E411 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E412 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E413 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2760000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E414 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1750000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E415 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E416 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3110000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E417 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E418 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E419 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E420 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1610000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E421 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0850000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E422 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1640000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E423 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0930000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E424 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0670000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E425 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E426 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1460000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E427 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E428 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2120000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E429 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1220000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E430 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E431 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E432 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E433 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0690000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E434 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E435 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E436 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E437 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3720000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E438 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2510000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E439 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E440 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E441 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1800000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E442 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E443 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3260000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E444 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1550000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E445 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0630000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E446 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E447 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E448 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0560000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E449 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E450 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1150000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E451 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0500000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E452 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E453 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E454 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | oil-based | NA | 160 | 1200 | No | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C106 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0570000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E455 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1210000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E456 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0950000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E457 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0430000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E458 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1150000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E459 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E460 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0330000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E461 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6640000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E462 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3120000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E463 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E464 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E465 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3780000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E466 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C106 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E467 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.5980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E468 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.4020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E469 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E470 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E471 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2460000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E472 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E473 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E474 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0620000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E475 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0430000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E476 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0800000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E477 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E478 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E479 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E480 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0700000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E481 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0340000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E482 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0650000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E483 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E484 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0320000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E485 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E486 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1080000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E487 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E488 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E489 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E490 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E491 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1260000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E492 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E493 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0520000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E494 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E495 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0760000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E496 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0490000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E497 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E498 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1130000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E499 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E500 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1520000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E501 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E502 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0610000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E503 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1220000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E504 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E505 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0490000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E506 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E507 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E508 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E509 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3570000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E510 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2100000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E511 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E512 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2560000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E513 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E514 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E515 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E516 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E517 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E518 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3410000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E519 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E520 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F011 | Taylor_2019 | 2019 | Australia | E521 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFHxS | 6 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 4 | 4 | ng/g | 0.9673000 | NA | 1.0026000 | sd | biological | 4 | 4 | 1.4745000 | NA | 1.7430000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | ML - check empty fields, why SE/SD field is NA? | 1.0026000 | 1.7430000 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E522 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 6 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6 | 6 | 84.5499000 | NA | 130.5000000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 133.7000000 | 130.5000000 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E523 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.4610420 | 43.3800 | C138 | Clean site | 3 | 3 | ng/g | 0.0894000 | NA | 0.0339000 | sd | biological | 3 | 3 | 0.1210000 | NA | 0.0390000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0339000 | 0.0390000 | 0.4610420 |
| F011 | Taylor_2019 | 2019 | Australia | E526 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFDS | 10 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 2 | 2 | ng/g | 0.1391000 | NA | 0.0247000 | sd | biological | 2 | 2 | 0.3760000 | NA | 0.0240000 | biological | 1 | ng/g | 0.030122517 | 0.10040839 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0247000 | 0.0240000 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E527 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | FOSA | 8 | NA | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 2 | 2 | ng/g | 0.0749000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 2 | 2 | 0.1985000 | NA | 0.0120000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0120000 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E528 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFHxS | 6 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C140 | Contaminated site | 5 | 5 | ng/g | 0.7841000 | NA | 0.9602000 | sd | biological | 5 | 5 | 0.8414000 | NA | 1.0420000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.9602000 | 1.0420000 | 0.7696748 |
| F011 | Taylor_2019 | 2019 | Australia | E529 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Contaminated site | 6 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6 | 6 | 70.8427000 | NA | 106.0000000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 133.7000000 | 106.0000000 | 0.7696748 |
| F011 | Taylor_2019 | 2019 | Australia | E530 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.9220839 | 43.3800 | C140 | Clean site | 2 | 2 | ng/g | 0.1090000 | NA | 0.0014000 | sd | biological | 2 | 2 | 0.2005000 | NA | 0.0730000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0014000 | 0.0730000 | 0.9220839 |
| F011 | Taylor_2019 | 2019 | Australia | E533 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | FOSA | 8 | NA | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Contaminated site | 4 | 4 | ng/g | 0.1070000 | NA | 0.0397000 | sd | biological | 4 | 4 | 0.2540000 | NA | 0.1320000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0397000 | 0.1320000 | 0.7696748 |
| F011 | Taylor_2019 | 2019 | Australia | E534 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxA | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 3 | 3 | ng/g | 0.1513000 | NA | 0.0306000 | sd | biological | 3 | 3 | 0.0729200 | NA | 0.0210000 | biological | 1 | ng/g | 0.028099467 | 0.093664888 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0306000 | 0.0210000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E535 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHpA | 7 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.2070000 | NA | 0.1445000 | sd | biological | 6 | 6 | 0.1086500 | NA | 0.0520000 | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.1445000 | 0.0520000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E536 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.4279000 | NA | 0.2601000 | sd | biological | 6 | 6 | 0.2316000 | NA | 0.1070000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2601000 | 0.1070000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E537 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 4 | 4 | ng/g | 0.0433000 | NA | 0.0137000 | sd | biological | 4 | 4 | 0.0712200 | NA | 0.0660000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0137000 | 0.0660000 | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E538 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 4 | 4 | ng/g | 0.1128000 | NA | 0.0093000 | sd | biological | 4 | 4 | 0.0579700 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0093000 | NA | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E539 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1047000 | NA | NA | sd | biological | 1 | 1 | 0.0579700 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E540 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 1 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1 | 1 | 0.1279700 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E541 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1230000 | NA | NA | sd | biological | 1 | 1 | 0.0802200 | <LOQ | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E542 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.5991000 | NA | 0.2053000 | sd | biological | 6 | 6 | 0.3865700 | NA | 0.0790000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2053000 | 0.0790000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E543 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1230000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1 | 1 | 0.0809900 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E544 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 5.0500000 | NA | 0.4637000 | sd | biological | 6 | 6 | 5.5333300 | NA | 0.8290000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.4637000 | 0.8290000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E545 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 6 | 6 | ng/g | 0.1917000 | NA | 0.2129000 | sd | biological | 6 | 6 | 0.1917100 | NA | 0.2360000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2129000 | 0.2360000 | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E548 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | FOSA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.3112000 | NA | 0.1413000 | sd | biological | 6 | 6 | 0.3215300 | NA | 0.0990000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.1413000 | 0.0990000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E549 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHpA | 7 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10 | 1 | 0.1279700 | NA | NA | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E550 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.2229000 | NA | 0.0668000 | sd | biological | 60 | 6 | 0.4689700 | NA | 0.1040000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0668000 | 0.1040000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E551 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.0910000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 60 | 6 | 0.2330900 | NA | 0.0370000 | biological | 1 | ng/g | 0.036013573 | 0.120045244 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0370000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E552 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.0854000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 50 | 5 | 0.1877100 | NA | 0.0530000 | biological | 1 | ng/g | 0.039417906 | 0.131393021 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0530000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E553 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHxS | 6 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 2.3305000 | NA | 1.3905000 | sd | biological | 60 | 6 | 6.3161900 | NA | 1.6280000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 1.3905000 | 1.6280000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E554 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 7.4167000 | NA | 2.8414000 | sd | biological | 60 | 6 | 16.1667000 | NA | 3.8690000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 2.8414000 | 3.8690000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E555 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 12.5376128 | 39.8800 | C144 | Clean site | 10 | 1 | ng/g | 0.0562000 | NA | 0.0133000 | sd | biological | 50 | 5 | 0.1180000 | NA | 0.0290000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0133000 | 0.0290000 | 12.5376128 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E557 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 30 | 1 | 1.7500000 | NA | 0.0500000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper) | ML - why “Se_technical_biological” is coded as “sd”? “If_technical_how_many” needs a number. Shared control between differend cooking methods | NA | NA | 0.9000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E558 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 30 | 1 | 2.9900000 | NA | 0.2200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.9000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E559 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 30 | 1 | 6.6200000 | NA | 0.1400000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.9000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E560 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 30 | 1 | 0.4400000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3600000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E561 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 30 | 1 | 1.1200000 | NA | 0.0300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3600000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E562 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 30 | 1 | 1.2700000 | NA | 0.0600000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3600000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E563 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 10 | 1 | 0.7000000 | LOD | NA | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4300000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E564 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 10 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4300000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E565 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 10 | 1 | 0.8300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4300000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E566 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 10 | 1 | 1.2400000 | NA | 0.0600000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4300000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E567 | Picarel | Spicara smaris | vertebrate | marine fish | 44.037940 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | NA | 20 | 1 | ng/g | 0.7000000 | NA | 0.0900000 | sd | technical | 30 | 1 | 1.3500000 | NA | 0.0800000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E568 | Picarel | Spicara smaris | vertebrate | marine fish | 44.037940 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | NA | 20 | 1 | ng/g | 20.3700000 | NA | 2.4700000 | sd | technical | 30 | 1 | 44.6900000 | NA | 3.9300000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E569 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 0.3500000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 30 | 1 | 0.7400000 | NA | 0.0900000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5200000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E570 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 1.0800000 | NA | 0.0300000 | sd | technical | 30 | 1 | 1.9800000 | NA | 0.0400000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5200000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E571 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 1.1600000 | NA | 0.0500000 | sd | technical | 30 | 1 | 3.0100000 | NA | 0.1300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5200000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E572 | Sardine | Sardina pilchardus | vertebrate | marine fish | 57.258065 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.8800000 | 340.9091 | C150 | NA | 14 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 30 | 1 | 0.9300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.8800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E573 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 30 | 1 | 0.5700000 | NA | 0.1100000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E574 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 30 | 1 | 0.5600000 | NA | 0.0700000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E575 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 30 | 1 | 0.7300000 | NA | 0.2000000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E576 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 30 | 1 | 1.3800000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E577 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 30 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E578 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 4.9400000 | NA | 0.2600000 | sd | technical | 40 | 1 | 14.8800000 | NA | 1.6100000 | technical | 1 | ng/g | 0.39 | 1.17 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E579 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 40 | 1 | 0.9900000 | NA | 0.2100000 | technical | 1 | ng/g | 0.6 | 1.82 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E580 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.2700000 | NA | 0.0700000 | sd | technical | 40 | 1 | 1.5200000 | NA | 0.1100000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E581 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.7300000 | NA | 0.0800000 | sd | technical | 40 | 1 | 1.8100000 | NA | 0.1900000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E582 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 2.7600000 | NA | 0.2100000 | sd | technical | 40 | 1 | 6.8200000 | NA | 0.2200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E583 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.3600000 | NA | 0.0900000 | sd | technical | 40 | 1 | 2.3100000 | NA | 0.0900000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E584 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.3700000 | NA | 0.1600000 | sd | technical | 40 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E585 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 5.1500000 | NA | 0.3900000 | sd | technical | 40 | 1 | 8.0200000 | NA | 0.4200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E586 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1950000 | <LOD | NA | sd | technical | 40 | 1 | 5.0600000 | NA | 0.1900000 | technical | 1 | ng/g | 0.39 | 1.17 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E587 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 40 | 1 | 0.5100000 | NA | 0.0400000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E588 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 40 | 1 | 1.0400000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E589 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 40 | 1 | 1.6500000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E590 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 40 | 1 | 1.5600000 | NA | 0.1700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E591 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 30 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 30 | 1 | 0.8300000 | NA | 0.0100000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E592 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 30 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 30 | 1 | 2.7300000 | NA | 0.1300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E593 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 30 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 30 | 1 | 3.5200000 | NA | 0.1000000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E594 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 30 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 30 | 1 | 6.2900000 | NA | 0.3400000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E595 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 30 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 30 | 1 | 0.4300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E596 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 30 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 30 | 1 | 0.6300000 | NA | 0.0200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E597 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 30 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 30 | 1 | 0.8700000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E598 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10 | 1 | 0.8200000 | NA | 0.0300000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E599 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 10 | 1 | 1.1100000 | NA | 0.1500000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E600 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 10 | 1 | 1.8900000 | NA | 0.0500000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E601 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFBS | 4 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 10 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E602 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 10 | 1 | 2.4000000 | NA | 0.1300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E603 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 30 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 30 | 1 | 0.8700000 | NA | 0.0300000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E604 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 30 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 30 | 1 | 1.7000000 | NA | 0.1300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E605 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 30 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 30 | 1 | 3.1900000 | NA | 0.0900000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E606 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFNA | 9 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 30 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 30 | 1 | 0.5000000 | NA | 0.0500000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E607 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 30 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 30 | 1 | 0.3450000 | <LOD | NA | NA | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E608 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 30 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 30 | 1 | 0.8200000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E609 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 30 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 30 | 1 | 10.2300000 | NA | 0.5300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E610 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFOA | 8 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 40 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 40 | 1 | 0.4000000 | NA | 0.0100000 | technical | 1 | ng/g | 0.6 | 1.82 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E611 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 40 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 40 | 1 | 1.0900000 | NA | 0.0200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E612 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 40 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 40 | 1 | 1.1900000 | NA | 0.1700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
Import phylogenetic information and calculate phylogenetic variance-covariance matrix
The phylogenetic tree was generated in the tree_cooked_fish_MA.Rmd document
tree <- read.tree(here("data", "phylogenetic_tree.tre")) # Import phylogenetic tree (see tree_cooked_fish_MA.Rmd for more details)
tree <- compute.brlen(tree) # Generate branch lengths
cor_tree <- vcv(tree, corr = T) # Generate phylogenetic variance-covariance matrix
dat$Phylogeny <- str_replace(dat$Species_Scientific, " ", "_") # Add the `phylogeny` column to the data frame
colnames(cor_tree) %in% dat$Phylogeny # Check correspondence between tip names and data frame## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEplot(tree)
Calculate effect sizes
The average coefficient of variation in PFAS concentration was calculated for each study and treatment, according to Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644. Then, these values were averaged across studies and used to calculate the lnRR corrected for small sample sizes (for formula, see the lnRR_func above)
aCV2 <- dat %>%
group_by(Study_ID) %>% # Group by study
summarise(CV2c = mean((SDc/Mc)^2, na.rm = T), # Calculate the squared coefficient of variation for control and experimental groups
CV2e = mean((SDe/Me)^2, na.rm = T)) %>%
ungroup() %>% # ungroup
summarise(aCV2c = mean(CV2c, na.rm = T), # Mean CV^2 for exp and control groups across studies
aCV2e = mean(CV2e, na.rm = T))
effect <- lnRR_func(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]],
rho = 0.5) # Calculate effect sizes
dat <- dat %>%
mutate(N_tilde = (Nc*Ne)/(Nc + Ne)) # Calculate the effective sample size
dat <- cbind(dat, effect) # Merge effect sizes with the data frame
VCV_lnRR <- make_VCV_matrix(dat, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # Because some effect sizes share the same control, we generated a variance-covariance matrix to account for correlated errors (i.e. effectively dividing the weight of the correlated estimates by half)Distribution of effect sizes
# mean
ggplot(dat, aes(x = lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.2) +
theme_classic()
# variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
theme_classic()
# log variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
scale_x_log10() + theme_classic()
Sample sizes
Table of sample sizes
dat %>%
summarise( # Calculate the number of effect sizes, studies and species for the main categorical variables
`Studies` = n_distinct(Study_ID),
`Species` = n_distinct(Species_common),
`PFAS type` = n_distinct(PFAS_type),
`Cohorts` = n_distinct(Cohort_ID),
`Effect sizes` = n_distinct(Effect_ID),
`Effect sizes (Oil-based)` = n_distinct(Effect_ID[Cooking_Category=="oil-based"]),
`Studies (Oil-based)` = n_distinct(Study_ID[Cooking_Category=="oil-based"]),
`Species (Oil-based)` = n_distinct(Species_common[Cooking_Category=="oil-based"]),
`Effect sizes (Water-based)` = n_distinct(Effect_ID[Cooking_Category=="water-based"]),
`Studies (Water-based)` = n_distinct(Study_ID[Cooking_Category=="water-based"]),
`Species (Water-based)` = n_distinct(Species_common[Cooking_Category=="water-based"]),
`Effect sizes (No liquid)` = n_distinct(Effect_ID[Cooking_Category=="No liquid"]),
`Studies (No liquid)` = n_distinct(Study_ID[Cooking_Category=="No liquid"]),
`Species (No liquid)` = n_distinct(Species_common[Cooking_Category=="No liquid"]),) -> table_sample_sizes
table_sample_sizes<-t(table_sample_sizes)
colnames(table_sample_sizes)<-"n (sample size)"
kable(table_sample_sizes) %>% kable_styling("striped", position="left")| n (sample size) | |
|---|---|
| Studies | 10 |
| Species | 39 |
| PFAS type | 18 |
| Cohorts | 153 |
| Effect sizes | 512 |
| Effect sizes (Oil-based) | 303 |
| Studies (Oil-based) | 7 |
| Species (Oil-based) | 28 |
| Effect sizes (Water-based) | 140 |
| Studies (Water-based) | 8 |
| Species (Water-based) | 23 |
| Effect sizes (No liquid) | 69 |
| Studies (No liquid) | 2 |
| Species (No liquid) | 14 |
Summary of the dataset
kable(summary(dat), "html") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Phylogeny | N_tilde | lnRR | var_lnRR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Length:512 | Length:512 | Min. :2008 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 6.77 | Length:512 | Min. : 3.000 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 75.0 | Min. : 120.0 | Length:512 | Length:512 | Length:512 | Min. : 0.341 | Min. : 0.0 | Min. : 0.00266 | Min. : 10.0 | Length:512 | Length:512 | Min. : 1.00 | Min. :1.000 | Length:512 | Min. : 0.002 | Length:512 | Min. : 0.0010 | Length:512 | Length:512 | Min. : 1.00 | Min. :1.000 | Min. : 0.0020 | Length:512 | Min. : 0.000 | Length:512 | Min. :1.000 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 0.0010 | Min. : 0.0010 | Length:512 | Min. : 0.500 | Min. :-6.0350 | Min. :0.01679 | |
| Class :character | Class :character | 1st Qu.:2014 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:14.45 | Class :character | 1st Qu.: 8.000 | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:100.0 | 1st Qu.: 600.0 | Class :character | Class :character | Class :character | 1st Qu.: 11.000 | 1st Qu.: 5.0 | 1st Qu.: 0.10004 | 1st Qu.: 10.0 | Class :character | Class :character | 1st Qu.: 5.00 | 1st Qu.:1.000 | Class :character | 1st Qu.: 0.160 | Class :character | 1st Qu.: 0.0010 | Class :character | Class :character | 1st Qu.: 5.00 | 1st Qu.:1.000 | 1st Qu.: 0.0940 | Class :character | 1st Qu.: 0.001 | Class :character | 1st Qu.:1.000 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.: 0.0354 | 1st Qu.: 0.0585 | Class :character | 1st Qu.: 2.500 | 1st Qu.:-0.8778 | 1st Qu.:0.08394 | |
| Mode :character | Mode :character | Median :2019 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :18.35 | Mode :character | Median : 8.000 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :160.0 | Median : 600.0 | Mode :character | Mode :character | Mode :character | Median : 300.000 | Median : 250.0 | Median : 2.50000 | Median : 70.0 | Mode :character | Mode :character | Median :10.00 | Median :1.000 | Mode :character | Median : 0.298 | Mode :character | Median : 0.0100 | Mode :character | Mode :character | Median :10.00 | Median :1.000 | Median : 0.2285 | Mode :character | Median : 0.020 | Mode :character | Median :3.000 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median : 0.1580 | Median : 0.1461 | Mode :character | Median : 5.000 | Median :-0.1671 | Median :0.08394 | |
| NA | NA | Mean :2017 | NA | NA | NA | NA | NA | NA | Mean :21.04 | NA | Mean : 8.994 | NA | NA | NA | NA | NA | Mean :161.3 | Mean : 733.3 | NA | NA | NA | Mean : 271.946 | Mean : 231.8 | Mean :13.58240 | Mean : 149.1 | NA | NA | Mean :10.34 | Mean :2.316 | NA | Mean : 3.494 | NA | Mean : 1.7676 | NA | NA | Mean :11.49 | Mean :2.371 | Mean : 3.2321 | NA | Mean : 1.822 | NA | Mean :2.481 | NA | NA | NA | NA | NA | NA | NA | NA | Mean : 4.4069 | Mean : 4.4491 | NA | Mean : 5.297 | Mean :-0.3637 | Mean :0.11794 | |
| NA | NA | 3rd Qu.:2019 | NA | NA | NA | NA | NA | NA | 3rd Qu.:21.31 | NA | 3rd Qu.:11.000 | NA | NA | NA | NA | NA | 3rd Qu.:175.0 | 3rd Qu.: 900.0 | NA | NA | NA | 3rd Qu.: 300.000 | 3rd Qu.: 300.0 | 3rd Qu.:30.00000 | 3rd Qu.: 178.4 | NA | NA | 3rd Qu.:10.00 | 3rd Qu.:5.000 | NA | 3rd Qu.: 1.083 | NA | 3rd Qu.: 0.1185 | NA | NA | 3rd Qu.:10.00 | 3rd Qu.:5.000 | 3rd Qu.: 1.0505 | NA | 3rd Qu.: 0.130 | NA | 3rd Qu.:3.000 | NA | NA | NA | NA | NA | NA | NA | NA | 3rd Qu.: 0.5600 | 3rd Qu.: 0.6516 | NA | 3rd Qu.: 5.000 | 3rd Qu.: 0.1849 | 3rd Qu.:0.16787 | |
| NA | NA | Max. :2020 | NA | NA | NA | NA | NA | NA | Max. :79.11 | NA | Max. :14.000 | NA | NA | NA | NA | NA | Max. :300.0 | Max. :1500.0 | NA | NA | NA | Max. :2500.000 | Max. :2500.0 | Max. :45.33092 | Max. :1000.0 | NA | NA | Max. :50.00 | Max. :6.000 | NA | Max. :86.689 | NA | Max. :133.7000 | NA | NA | Max. :60.00 | Max. :6.000 | Max. :134.4379 | NA | Max. :130.500 | NA | Max. :4.000 | NA | NA | NA | NA | NA | NA | NA | NA | Max. :133.7000 | Max. :130.5000 | NA | Max. :25.000 | Max. : 3.4622 | Max. :0.83936 | |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :284 | NA | NA | NA | NA | NA | NA | NA | NA’s :6 | NA’s :56 | NA | NA | NA | NA’s :114 | NA’s :45 | NA’s :88 | NA’s :106 | NA | NA | NA | NA | NA | NA | NA | NA’s :53 | NA | NA | NA | NA | NA | NA | NA’s :55 | NA | NA’s :198 | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :330 | NA’s :328 | NA | NA | NA | NA |
Intercept meta-analytical model
Determine the random effect structure
Cohort_ID explains virtually no variance in the model. Hence, it was removed from the model. All the other random effects explained significant variance and were kept in subsequent models
MA_all_rand_effects <- rma.mv(lnRR, VCV_lnRR, # Add `VCV_lnRR` to account for correlated errors errors between cohorts (shared_controls)
random = list(~1|Study_ID, # Identity of the study
~1|Phylogeny, # Phylogenetic correlation
~1|Cohort_ID, # Identity of the cohort (shared controls)
~1|Species_common, # Non-phylogenetic correlation between species
~1|PFAS_type, # Type of PFAS
~1|Effect_ID), # Effect size identity
R= list(Phylogeny = cor_tree), # Assign the 'Phylogeny' argument to the phylogenetic variance-covariance matrix
test = "t",
data = dat,
sparse = TRUE)
summary(MA_all_rand_effects) # Cohort ID does not explain any variance ##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -625.3701 1250.7402 1264.7402 1294.3947 1264.9628
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5837 0.7640 10 no Study_ID no
## sigma^2.2 0.0000 0.0005 38 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 153 no Cohort_ID no
## sigma^2.4 0.2222 0.4714 39 no Species_common no
## sigma^2.5 0.0973 0.3119 18 no PFAS_type no
## sigma^2.6 0.5003 0.7073 512 no Effect_ID no
##
## Test for Heterogeneity:
## Q(df = 511) = 11056.9620, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3259 0.2856 -1.1413 511 0.2543 -0.8871 0.2352
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Intercept meta-analytical model and percentage of heterogeneity
MA_model <- rma.mv(lnRR, VCV_lnRR,
random = list(~1|Study_ID,
~1|Phylogeny, # Removed Cohort_ID
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = dat,
sparse = TRUE)
summary(MA_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -625.3701 1250.7402 1262.7402 1288.1584 1262.9068
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5837 0.7640 10 no Study_ID no
## sigma^2.2 0.0000 0.0005 38 no Phylogeny yes
## sigma^2.3 0.2222 0.4714 39 no Species_common no
## sigma^2.4 0.0973 0.3119 18 no PFAS_type no
## sigma^2.5 0.5003 0.7073 512 no Effect_ID no
##
## Test for Heterogeneity:
## Q(df = 511) = 11056.9620, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3259 0.2856 -1.1413 511 0.2543 -0.8871 0.2352
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1round(i2_ml(MA_model)*100,2) # Percentage of heterogeneity explained by each random effect## I2_total I2_Study_ID I2_Phylogeny I2_Species_common
## 94.62 39.35 0.00 14.98
## I2_PFAS_type I2_Effect_ID
## 6.56 33.73# plot
orchard_plot(MA_model, mod = "Int", xlab = "lnRR", alpha=0.4) + # Orchard plot
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = "darkorange")+ # change colours
scale_fill_manual(values="darkorange")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
save(MA_model, MA_all_rand_effects, file = here("Rdata", "int_MA_models.RData")) # save the models Single moderator meta-regressions
Function to run all models with the same structure
run_model<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data
, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = data,
sparse=TRUE) # Make the model run faster
}Function to run plots with the same structure
plot_continuous<-function(data, model, moderator, xlab){
pred<-predict.rma(model)
data %>% mutate(fit=pred$pred,
ci.lb=pred$ci.lb,
ci.ub=pred$ci.ub,
pr.lb=pred$cr.lb,
pr.ub=pred$cr.ub) %>% # Add confidence intervals, mean predictions and prediction intervals
ggplot(aes(x = moderator, y = lnRR)) +
geom_ribbon(aes(ymin = pr.lb, ymax = pr.ub, color = NULL), alpha = .075) + # Shaded area for prediction intervals
geom_ribbon(aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = .2) + # Shaded area for confidence intervals
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) + # Points scaled by precision
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
geom_line(aes(y = fit), size = 1.5)+ # Regression line
labs(x = xlab, y = "lnRR", size = "Precison (1/SE)") +
theme_bw() +
scale_size_continuous(range=c(1,9))+ # Point scaling
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
}Single-moderator models
All continuous variables were z-transformed
Cooking time
# Length_cooking_time_in_s
time_model <- run_model(dat, ~scale(Length_cooking_time_in_s)) # z-transformed
summary(time_model)##
## Multivariate Meta-Analysis Model (k = 456; method: REML)
##
## logLik Deviance AIC BIC AICc
## -515.6836 1031.3672 1045.3672 1074.1939 1045.6183
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5316 0.7291 9 no Study_ID no
## sigma^2.2 0.0000 0.0002 30 no Phylogeny yes
## sigma^2.3 0.1715 0.4142 30 no Species_common no
## sigma^2.4 0.0982 0.3133 17 no PFAS_type no
## sigma^2.5 0.4092 0.6397 456 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 454) = 6658.0429, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 454) = 27.6416, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5548 0.2875 -1.9301 454 0.0542
## scale(Length_cooking_time_in_s) -0.2567 0.0488 -5.2575 454 <.0001
## ci.lb ci.ub
## intrcpt -1.1197 0.0101 .
## scale(Length_cooking_time_in_s) -0.3526 -0.1607 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(time_model) # Estimate R squared## R2_marginal R2_coditional
## 0.05161056 0.67939517# Plot
dat.time <- filter(dat, Length_cooking_time_in_s != "NA") # Need to remove the NAs from the data
plot_continuous(dat.time, time_model, dat.time$Length_cooking_time_in_s, "Cooking time (s)")
Ratio between tissue sample and liquid volume, with dry cooking coded as NA
# Ratio_liquid_fish
dat <- dat %>%
mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category == "No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is 'No liquid', otherwise keep the same value of Ratio_liquid_fish
volume_model <- run_model(dat, ~scale(log(Ratio_liquid_fish))) # logged and z-transformed
summary(volume_model)##
## Multivariate Meta-Analysis Model (k = 424; method: REML)
##
## logLik Deviance AIC BIC AICc
## -531.2435 1062.4869 1076.4869 1104.8020 1076.7575
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5490 0.7410 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 34 no Phylogeny yes
## sigma^2.3 0.1517 0.3895 35 no Species_common no
## sigma^2.4 0.1126 0.3355 18 no PFAS_type no
## sigma^2.5 0.5452 0.7384 424 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 422) = 8122.5881, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 422) = 4.1546, p-val = 0.0421
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.4422 0.2994 -1.4771 422 0.1404 -1.0308
## scale(log(Ratio_liquid_fish)) -0.2578 0.1265 -2.0383 422 0.0421 -0.5064
## ci.ub
## intrcpt 0.1463
## scale(log(Ratio_liquid_fish)) -0.0092 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(volume_model)## R2_marginal R2_coditional
## 0.04664436 0.61740353# Plot
dat.volume <- filter(dat, Ratio_liquid_fish != "NA")
plot_continuous(dat.volume, volume_model, log(dat.volume$Ratio_liquid_fish), "ln (Liquid volume to tissue sample ratio)")
Ratio between tissue sample and liquid volume, with dry cooking coded as 0
volume0_model <- run_model(dat, ~scale(log(Ratio_liquid_fish_0 + 1))) # logged and z-transformed after adding 1
summary(volume0_model)##
## Multivariate Meta-Analysis Model (k = 493; method: REML)
##
## logLik Deviance AIC BIC AICc
## -596.7745 1193.5490 1207.5490 1236.9241 1207.7809
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6069 0.7790 8 no Study_ID no
## sigma^2.2 0.0000 0.0002 34 no Phylogeny yes
## sigma^2.3 0.2128 0.4613 35 no Species_common no
## sigma^2.4 0.1216 0.3487 18 no PFAS_type no
## sigma^2.5 0.4910 0.7007 493 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 491) = 9498.4285, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 491) = 4.3306, p-val = 0.0380
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.4388 0.3164 -1.3868 491 0.1661
## scale(log(Ratio_liquid_fish_0 + 1)) -0.1156 0.0555 -2.0810 491 0.0380
## ci.lb ci.ub
## intrcpt -1.0604 0.1829
## scale(log(Ratio_liquid_fish_0 + 1)) -0.2247 -0.0065 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(volume0_model)## R2_marginal R2_coditional
## 0.009240908 0.660359049# Plot
dat.volume0 <- filter(dat, Ratio_liquid_fish_0 != "NA")
plot_continuous(dat.volume0, volume0_model, log(dat.volume0$Ratio_liquid_fish_0 +
1), "ln (Liquid volume to tissue sample ratio) + 1")
Cooking temperature
# Temperature_in_Celsius
temp_model <- run_model(dat, ~scale(Temperature_in_Celsius)) # z-transformed
summary(temp_model)##
## Multivariate Meta-Analysis Model (k = 506; method: REML)
##
## logLik Deviance AIC BIC AICc
## -616.8140 1233.6280 1247.6280 1277.1861 1247.8538
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5782 0.7604 10 no Study_ID no
## sigma^2.2 0.0000 0.0003 38 no Phylogeny yes
## sigma^2.3 0.2202 0.4692 39 no Species_common no
## sigma^2.4 0.0938 0.3062 18 no PFAS_type no
## sigma^2.5 0.5018 0.7084 506 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 504) = 10706.4270, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 504) = 0.0242, p-val = 0.8765
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.3110 0.2861 -1.0870 504 0.2776 -0.8730
## scale(Temperature_in_Celsius) 0.0112 0.0721 0.1555 504 0.8765 -0.1304
## ci.ub
## intrcpt 0.2511
## scale(Temperature_in_Celsius) 0.1528
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(temp_model)## R2_marginal R2_coditional
## 9.013051e-05 6.400389e-01# Plot
dat.temp <- filter(dat, Temperature_in_Celsius != "NA")
plot_continuous(dat.temp, temp_model, dat.temp$Temperature_in_Celsius, "Cooking temperature")
PFAS carbon chain length
# PFAS_carbon_chain
PFAS_model <- run_model(dat, ~PFAS_carbon_chain)
summary(PFAS_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -623.8826 1247.7652 1261.7652 1291.4061 1261.9884
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5826 0.7633 10 no Study_ID no
## sigma^2.2 0.0000 0.0010 38 no Phylogeny yes
## sigma^2.3 0.2236 0.4728 39 no Species_common no
## sigma^2.4 0.1016 0.3188 18 no PFAS_type no
## sigma^2.5 0.5005 0.7075 512 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 10984.3510, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.1787, p-val = 0.6726
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.4443 0.3994 -1.1124 510 0.2665 -1.2289 0.3403
## PFAS_carbon_chain 0.0130 0.0308 0.4228 510 0.6726 -0.0474 0.0734
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(PFAS_model)## R2_marginal R2_coditional
## 0.0006517908 0.6448264863plot_continuous(dat, PFAS_model, dat$PFAS_carbon_chain, "PFAS carbon chain length")
Cooking category
# Cooking_Category
category_model<-run_model(dat, ~Cooking_Category-1)
summary(category_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -622.3146 1244.6292 1260.6292 1294.4888 1260.9172
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5872 0.7663 10 no Study_ID no
## sigma^2.2 0.0000 0.0003 38 no Phylogeny yes
## sigma^2.3 0.2237 0.4730 39 no Species_common no
## sigma^2.4 0.0988 0.3144 18 no PFAS_type no
## sigma^2.5 0.4996 0.7069 512 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 509) = 10895.4833, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 509) = 1.2466, p-val = 0.2922
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Cooking_CategoryNo liquid -0.2055 0.3075 -0.6684 509 0.5042 -0.8097
## Cooking_Categoryoil-based -0.3843 0.2932 -1.3105 509 0.1906 -0.9603
## Cooking_Categorywater-based -0.2964 0.2915 -1.0169 509 0.3097 -0.8690
## ci.ub
## Cooking_CategoryNo liquid 0.3986
## Cooking_Categoryoil-based 0.1918
## Cooking_Categorywater-based 0.2762
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(category_model)## R2_marginal R2_coditional
## 0.00290664 0.64651952# plot
orchard_plot(category_model, mod = "Cooking_Category", xlab = "lnRR", alpha=0.4)+
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3"))+ # change colours
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))
Percentage of moisture loss
This analysis is a posteriori and will only be presented in supplement.
# Moisture_loss_in_percent
moisture_model <- run_model(dat, ~scale(Moisture_loss_in_percent))
summary(moisture_model)##
## Multivariate Meta-Analysis Model (k = 228; method: REML)
##
## logLik Deviance AIC BIC AICc
## -223.7733 447.5465 461.5465 485.4903 462.0603
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0802 0.2832 6 no Study_ID no
## sigma^2.2 0.2262 0.4756 18 no Phylogeny yes
## sigma^2.3 0.1339 0.3659 18 no Species_common no
## sigma^2.4 0.0093 0.0965 17 no PFAS_type no
## sigma^2.5 0.3105 0.5573 228 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 226) = 3369.7752, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 226) = 0.0866, p-val = 0.7688
##
## Model Results:
##
## estimate se tval df pval
## intrcpt 0.5257 0.3278 1.6039 226 0.1101
## scale(Moisture_loss_in_percent) -0.0192 0.0654 -0.2943 226 0.7688
## ci.lb ci.ub
## intrcpt -0.1202 1.1717
## scale(Moisture_loss_in_percent) -0.1481 0.1096
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(moisture_model)## R2_marginal R2_coditional
## 0.0004866973 0.5916335142# Plot
dat.moisture <- filter(dat, Moisture_loss_in_percent != "NA")
plot_continuous(dat.moisture, moisture_model, dat.moisture$Moisture_loss_in_percent,
"Percentage of moisture loss")
save(category_model, PFAS_model, temp_model, time_model, volume_model, volume0_model,
moisture_model, file = here("Rdata", "single_mod_models.RData")) # Save modelsFull models
Full model with Ratio_liquid_fish as NA for the dry cooking category
# Testing cooking categories
full_model <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)),
random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
~1 | Effect_ID), R = list(Phylogeny = cor_tree), test = "t", data = dat,
sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)
summary(full_model)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -429.8045 859.6090 881.6090 924.8929 882.3303
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4275 0.6538 7 no Study_ID no
## sigma^2.2 0.2960 0.5441 26 no Phylogeny yes
## sigma^2.3 0.0565 0.2376 26 no Species_common no
## sigma^2.4 0.1225 0.3500 17 no PFAS_type no
## sigma^2.5 0.4093 0.6397 384 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 378) = 5367.5620, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 378) = 9.4240, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.7901 0.4174 -1.8930 378 0.0591
## Cooking_Categoryoil-based 0.1177 0.1701 0.6921 378 0.4893
## scale(Temperature_in_Celsius) -0.3329 0.1310 -2.5406 378 0.0115
## scale(Length_cooking_time_in_s) -0.3298 0.0560 -5.8926 378 <.0001
## scale(PFAS_carbon_chain) 0.0643 0.0798 0.8054 378 0.4211
## scale(log(Ratio_liquid_fish)) -0.8149 0.1822 -4.4721 378 <.0001
## ci.lb ci.ub
## intrcpt -1.6108 0.0306 .
## Cooking_Categoryoil-based -0.2167 0.4522
## scale(Temperature_in_Celsius) -0.5905 -0.0753 *
## scale(Length_cooking_time_in_s) -0.4398 -0.2197 ***
## scale(PFAS_carbon_chain) -0.0927 0.2212
## scale(log(Ratio_liquid_fish)) -1.1733 -0.4566 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model)## R2_marginal R2_coditional
## 0.3178842 0.7871710save(full_model, file = here("Rdata", "full_model.RData"))Full model with Ratio_liquid_fish as 0 for the dry cooking category
full_model0 <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 +
1)), random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
~1 | Effect_ID), R = list(Phylogeny = cor_tree), test = "t", data = dat, sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)
summary(full_model0)##
## Multivariate Meta-Analysis Model (k = 431; method: REML)
##
## logLik Deviance AIC BIC AICc
## -454.8862 909.7723 933.7723 982.3691 934.5315
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4384 0.6621 7 no Study_ID no
## sigma^2.2 0.3862 0.6215 26 no Phylogeny yes
## sigma^2.3 0.0391 0.1978 26 no Species_common no
## sigma^2.4 0.1354 0.3680 17 no PFAS_type no
## sigma^2.5 0.3572 0.5976 431 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 424) = 5465.2510, p-val < .0001
##
## Test of Moderators (coefficients 2:7):
## F(df1 = 6, df2 = 424) = 12.1936, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -2.3598 0.5351 -4.4097 424 <.0001
## Cooking_Categoryoil-based 1.6600 0.3483 4.7661 424 <.0001
## Cooking_Categorywater-based 1.9433 0.3898 4.9855 424 <.0001
## scale(Temperature_in_Celsius) 0.0133 0.0976 0.1365 424 0.8915
## scale(Length_cooking_time_in_s) -0.3714 0.0497 -7.4679 424 <.0001
## scale(PFAS_carbon_chain) 0.0657 0.0809 0.8123 424 0.4170
## scale(log(Ratio_liquid_fish_0 + 1)) -0.8604 0.1541 -5.5829 424 <.0001
## ci.lb ci.ub
## intrcpt -3.4117 -1.3080 ***
## Cooking_Categoryoil-based 0.9754 2.3446 ***
## Cooking_Categorywater-based 1.1772 2.7095 ***
## scale(Temperature_in_Celsius) -0.1784 0.2051
## scale(Length_cooking_time_in_s) -0.4691 -0.2736 ***
## scale(PFAS_carbon_chain) -0.0933 0.2247
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1634 -0.5575 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model0)## R2_marginal R2_coditional
## 0.4258950 0.8488249save(full_model0, file = here("Rdata", "full_model.RData"))Check collinearity of predictors
## Check for collinerarity - seems fine
vif(full_model)##
## Cooking_Categoryoil-based scale(Temperature_in_Celsius)
## 2.2542 3.4186
## scale(Length_cooking_time_in_s) scale(PFAS_carbon_chain)
## 1.0493 1.0000
## scale(log(Ratio_liquid_fish))
## 1.8669vif(full_model0)##
## Cooking_Categoryoil-based Cooking_Categorywater-based
## 14.3698 14.2052
## scale(Temperature_in_Celsius) scale(Length_cooking_time_in_s)
## 2.1185 1.0724
## scale(PFAS_carbon_chain) scale(log(Ratio_liquid_fish_0 + 1))
## 1.0000 9.4572dat %>%
select(Temperature_in_Celsius, Length_cooking_time_in_s, PFAS_carbon_chain, Ratio_liquid_fish) %>%
ggpairs() # Estimate correlations between the variables
Conditional analyses
Inspection of the plots highlighted potential significant decreases in PFAS content with increased cooking time and volume of cooking. Hence, here we used emmeans (download from remotes::install_github(“rvlenth/emmeans”, dependencies = TRUE, build_opts = "")) to generate marginalised means at specified values of the different predictors. Such analysis enable the quantification of the mean effect size after controlling for different values of the moderators.
Note that these analyses were not performed separately using full models with Ratio_liquid_fish taken as NA or 0. Indeed, a full model containing the dry cooking category and the liquid ratio would extrapolate predictions for the dry cooking category at the mean liquid ratio; which is incorrect. Therefore, all full models were ran with the data containing NA for the Ratio_liquid_fish of the dry cooking method; and separate models were ran with a data subset only containing the dry cooking method.
Full model
# Full model in original units (no z-transformation)
dat$log_Ratio_liquid_fish <- log(dat$Ratio_liquid_fish)
full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), but without the 'No
# liquid' data This model will be used for conditional analyses on the volume
# of liquid, where the data without liquid is irrelevant
dat_oil_water <- filter(dat, Cooking_Category != "No liquid")
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), with Ratio_liquid_fish_0
dat$log_Ratio_liquid_fish0 <- log(dat$Ratio_liquid_fish_0 + 1)
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)
# Data subset only containing the data with the dry cooking method. Here, only
# the cooking time was added because the liquid ratio, cooking temperature, and
# PFAS carbon chain length do not have sufficient variability.
dat_dry <- filter(dat, Cooking_Category == "No liquid")
full_model_org_units_dry <- run_model(dat_dry, ~Length_cooking_time_in_s)
save(full_model_org_units, full_model_org_units_dry, full_model_org_units0, full_model_org_units_oil_water,
file = here("Rdata", "full_models_org_units.RData"))Overall marginalised mean
Overall mean with Ratio_liquid_fish taken as NA for the dry cooking category
res <- marginal_means(model = full_model_org_units, data = dat, mod = "1")
res$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.7572305 -1.556052 0.04159075 -3.146689 1.632228Overall mean with Ratio_liquid_fish taken as 0 for the dry cooking category
res0 <- marginal_means(model = full_model_org_units0, data = dat, mod = "1")
res0$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -1.264438 -2.13653 -0.3923459 -3.714103 1.185226Marginal means for different cooking categories
Overall mean for the water- and oil-based cooking
res_cat <- marginal_means(full_model_org_units, data = dat, mod = "1", by = "Cooking_Category")
res_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt oil-based -0.6983732 -1.508493 0.111746214 -3.091632 1.694886
## 2 Intrcpt water-based -0.8160879 -1.638197 0.006021405 -3.213432 1.581256orchard_plot(res_cat, xlab = "lnRR", condition.lab = "Cooking Category")
Overall mean for the dry cooking
res_dry <- marginal_means(full_model_org_units_dry, data = dat, mod = "1")
res_dry$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.5699711 -1.000248 -0.1396944 -1.349651 0.2097082orchard_plot(res_dry, xlab = "lnRR", condition.lab = "Cooking Category")
Marginal means for pre-determined cooking times
Here, we generate estimates at cooking times of 2, 10, and 25 min.
Marginal means for pre-determined cooking times, with Ratio_liquid_fish taken as NA for the dry cooking category
res_cooking_time <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,
600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.08017252 -0.8956537 0.7353087 -2.475252 2.314907
## 2 Intrcpt 600 -0.62181892 -1.4188934 0.1752556 -3.010694 1.767056
## 3 Intrcpt 1500 -1.63740593 -2.5067525 -0.7680593 -4.051357 0.776545orchard_plot(res_cooking_time, xlab = "lnRR", condition.lab = "Cooking time (sec)")
Marginal means for pre-determined cooking times, with Ratio_liquid_fish taken as 0 for the dry cooking category
res_cooking_time0 <- marginal_means(full_model_org_units0, data = dat, mod = "1",
at = list(Length_cooking_time_in_s = c(120, 600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.4779287 -1.351086 0.3952287 -2.927973 1.9721153
## 2 Intrcpt 600 -1.0878893 -1.955934 -0.2198443 -3.536116 1.3603374
## 3 Intrcpt 1500 -2.2315654 -3.167381 -1.2957503 -4.704633 0.2415019orchard_plot(res_cooking_time0, xlab = "lnRR", condition.lab = "Cooking time (sec)")
Marginalised means for the water- and oil-based cooking categories
res_cooking_time_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category",
at = list(Length_cooking_time_in_s = c(120, 600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 120 -0.02131516 -0.8514695 0.8088392 -2.421430 2.3787998
## 2 Water-based 120 -0.13902987 -0.9737688 0.6957091 -2.540734 2.2626747
## 3 Oil-based 600 -0.56296156 -1.3720966 0.2461735 -2.955888 1.8299645
## 4 Water-based 600 -0.68067628 -1.5003606 0.1390080 -3.077190 1.7158374
## 5 Oil-based 1500 -1.57854857 -2.4538669 -0.7032302 -3.994657 0.8375594
## 6 Water-based 1500 -1.69626328 -2.5914007 -0.8011259 -4.119622 0.7270952orchard_plot(res_cooking_time_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)")
Marginalised means for the dry cooking category
res_cooking_time_dry <- marginal_means(full_model_org_units_dry, data = dat, at = list(Length_cooking_time_in_s = c(120,
600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time_dry$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 0.3317270 -0.2248785 0.88833238 -0.5241769 1.1876308
## 2 Intrcpt 600 -0.3552811 -0.8018081 0.09124584 -1.1440448 0.4334826
## 3 Intrcpt 1500 -1.6434213 -2.1519525 -1.13489008 -2.4688701 -0.8179724orchard_plot(res_cooking_time_dry, xlab = "lnRR", condition.lab = "Cooking time (sec)")
Marginal means for different volumes of liquid
Ratio_liquid_fish taken as NA for the dry cooking category
Here, we generate marginalised estimates at volumes of liquid of ~0.1mL/g of tissue, ~10 ml/g of tissue, or 45 mL/g of tissue. We did not look at the means for different cooking categories because they are inherently different in the volume of liquid used. We also only used the data on oil and water because the “No liquid” category is not relevant for this analysis when considering Ratio_liquid_fish as NA.
res_volume <- marginal_means(full_model_org_units_oil_water, data = dat_oil_water,
mod = "1", at = list(log_Ratio_liquid_fish = c(log(0.1), log(10), log(45))),
by = "log_Ratio_liquid_fish")
res_volume$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -2.302585 -0.05967101 -0.9192784 0.7999364 -2.470132 2.3507896
## 2 Intrcpt 2.302585 -1.23336241 -2.0563665 -0.4103584 -3.631014 1.1642888
## 3 Intrcpt 3.806662 -1.61669737 -2.4956750 -0.7377197 -4.034133 0.8007387orchard_plot(res_volume, xlab = "lnRR", condition.lab = "ln(Liquid volume to tissue sample ratio) (mL/g)")
Ratio_liquid_fish taken as 0 for the dry cooking category
Here, we generate marginalised estimates at volumes of liquid of 0mL/g of tissue (dry cooking), ~10 ml/g of tissue, or 45 mL/g of tissue.
res_volume0 <- marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(log_Ratio_liquid_fish0 = c(0,
log(10 + 1), log(45 + 1))), by = "log_Ratio_liquid_fish0")
res_volume0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 0.000000 -0.3635577 -1.233235 0.5061198 -2.812364 2.08524832
## 2 Intrcpt 2.397895 -1.6778653 -2.588815 -0.7669157 -4.141631 0.78590033
## 3 Intrcpt 3.828641 -2.4620699 -3.500178 -1.4239621 -4.975629 0.05148929orchard_plot(res_volume0, xlab = "lnRR", condition.lab = "ln(Liquid volume to tissue sample ratio) (mL/g)")
Marginal means for different PFAS carbon chains
Overall mean at the mean of each other predictor, with Ratio_liquid_fish taken as NA for the dry cooking category
Here, we generate marginalized estimates for PFAS of 3, 6, and 12 carbon chains
res_PFAS <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(PFAS_carbon_chain = c(3,
6, 12)), by = "PFAS_carbon_chain")
res_PFAS$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -0.9244650 -1.824179 -0.02475128 -3.349518 1.500588
## 2 Intrcpt 6 -0.8417098 -1.668156 -0.01526386 -3.240545 1.557125
## 3 Intrcpt 12 -0.6761994 -1.497777 0.14537834 -3.073361 1.720963orchard_plot(res_PFAS, xlab = "lnRR", condition.lab = "PFAS carbon chain")
Overall mean at the mean of each other predictor, with Ratio_liquid_fish taken as 0 for the dry cooking category
res_PFAS0 <- marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(PFAS_carbon_chain = c(3,
6, 12)), by = "PFAS_carbon_chain")
res_PFAS0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -1.432143 -2.400325 -0.4639609 -3.917639 1.053352
## 2 Intrcpt 6 -1.347538 -2.245873 -0.4492031 -3.806668 1.111591
## 3 Intrcpt 12 -1.178328 -2.071472 -0.2851846 -3.635566 1.278910orchard_plot(res_PFAS0, xlab = "lnRR", condition.lab = "PFAS carbon chain")
Marginalised mean estimate for the water- and oil-based cooking categories
res_PFAS_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category",
at = list(PFAS_carbon_chain = c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 3 -0.8656077 -1.775510 0.04429441 -3.294459 1.563243
## 2 Water-based 3 -0.9833224 -1.903634 -0.06301042 -3.416092 1.449448
## 3 Oil-based 6 -0.7828525 -1.620302 0.05459708 -3.185500 1.619796
## 4 Water-based 6 -0.9005672 -1.749467 -0.05166768 -3.307230 1.506096
## 5 Oil-based 12 -0.6173420 -1.449834 0.21514970 -3.018266 1.783582
## 6 Water-based 12 -0.7350567 -1.579369 0.10925582 -3.140105 1.669992orchard_plot(res_PFAS_cat, xlab = "lnRR", condition.lab = "PFAS carbon chain")
Sub-group analyses for each cooking category
Here, we investigated whether the effect of the continuous moderators on lnRR vary depending on the cooking category. Hence, we performed subset analyses for each cooking category.
Oil-based cooking
Subset data and update function
oil_dat<-filter(dat, Cooking_Category=="oil-based")
include <- row.names(cor_tree) %in% oil_dat$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_oil <- cor_tree[include, include] # Only include the species that match the reduced data set
run_model_oil<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_oil), # cor_tree_oil here
test = "t",
data = data)
}Full model
full_model_oil <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))
summary(full_model_oil)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -176.0279 352.0558 372.0558 407.5854 372.9465
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1147 0.3387 6 no Study_ID no
## sigma^2.2 0.0000 0.0000 19 no Phylogeny yes
## sigma^2.3 0.0252 0.1586 19 no Species_common no
## sigma^2.4 0.0485 0.2203 16 no PFAS_type no
## sigma^2.5 0.1293 0.3596 263 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 258) = 1004.4883, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 17.9272, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5750 0.1811 -3.1746 258 0.0017
## scale(Temperature_in_Celsius) -0.0868 0.1173 -0.7404 258 0.4598
## scale(Length_cooking_time_in_s) -0.3782 0.0468 -8.0906 258 <.0001
## scale(PFAS_carbon_chain) 0.1283 0.0613 2.0923 258 0.0374
## scale(log(Ratio_liquid_fish)) -0.2048 0.2022 -1.0129 258 0.3121
## ci.lb ci.ub
## intrcpt -0.9317 -0.2183 **
## scale(Temperature_in_Celsius) -0.3178 0.1441
## scale(Length_cooking_time_in_s) -0.4703 -0.2862 ***
## scale(PFAS_carbon_chain) 0.0076 0.2491 *
## scale(log(Ratio_liquid_fish)) -0.6030 0.1934
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_oil)## R2_marginal R2_coditional
## 0.4421662 0.7730077save(full_model_oil, file = here("Rdata", "full_model_oil.RData"))Full model, with Ratio_liquid_fish taken as 0 for the dry cooking category
full_model_oil0 <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_oil0)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -174.9078 349.8156 369.8156 405.3452 370.7062
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1110 0.3332 6 no Study_ID no
## sigma^2.2 0.0000 0.0000 19 no Phylogeny yes
## sigma^2.3 0.0225 0.1501 19 no Species_common no
## sigma^2.4 0.0509 0.2257 16 no PFAS_type no
## sigma^2.5 0.1287 0.3587 263 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 258) = 1001.1583, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 18.4863, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5811 0.1787 -3.2522 258 0.0013
## scale(Temperature_in_Celsius) -0.0158 0.0786 -0.2012 258 0.8407
## scale(Length_cooking_time_in_s) -0.3791 0.0465 -8.1480 258 <.0001
## scale(PFAS_carbon_chain) 0.1287 0.0621 2.0738 258 0.0391
## scale(log(Ratio_liquid_fish_0 + 1)) -0.3162 0.1809 -1.7479 258 0.0817
## ci.lb ci.ub
## intrcpt -0.9330 -0.2293 **
## scale(Temperature_in_Celsius) -0.1706 0.1390
## scale(Length_cooking_time_in_s) -0.4708 -0.2875 ***
## scale(PFAS_carbon_chain) 0.0065 0.2509 *
## scale(log(Ratio_liquid_fish_0 + 1)) -0.6724 0.0400 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_oil0)## R2_marginal R2_coditional
## 0.5608473 0.8195491save(full_model_oil0, file = here("Rdata", "full_model_oil0.RData"))Water-based cooking
Subset data and updating functions
water_dat<-filter(dat, Cooking_Category=="water-based")
include <- row.names(cor_tree) %in% water_dat$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_water <- cor_tree[include, include] # Only include the species that match the reduced data set
run_model_water<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_water), # cor_tree_water here
test = "t",
data = data)
}Full model
full_model_water <- run_model_water(water_dat, ~scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))
summary(full_model_water)##
## Multivariate Meta-Analysis Model (k = 121; method: REML)
##
## logLik Deviance AIC BIC AICc
## -178.5156 357.0312 375.0312 399.8908 376.7134
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5949 0.7713 6 no Study_ID no
## sigma^2.2 0.0000 0.0002 19 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 19 no Species_common no
## sigma^2.4 0.5412 0.7357 15 no PFAS_type no
## sigma^2.5 0.9346 0.9667 121 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 117) = 4136.3260, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.2361, p-val = 0.0070
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.3265 0.4166 -3.1844 117 0.0019
## scale(Length_cooking_time_in_s) -0.3738 0.1579 -2.3674 117 0.0196
## scale(PFAS_carbon_chain) -0.0488 0.1811 -0.2696 117 0.7879
## scale(log(Ratio_liquid_fish)) -0.6521 0.2517 -2.5911 117 0.0108
## ci.lb ci.ub
## intrcpt -2.1515 -0.5015 **
## scale(Length_cooking_time_in_s) -0.6865 -0.0611 *
## scale(PFAS_carbon_chain) -0.4075 0.3099
## scale(log(Ratio_liquid_fish)) -1.1506 -0.1537 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_water)## R2_marginal R2_coditional
## 0.2135753 0.6450533Full model, with Ratio_liquid_fish taken as 0 for the dry cooking category
full_model_water0 <- run_model_water(water_dat, ~scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_water0)##
## Multivariate Meta-Analysis Model (k = 121; method: REML)
##
## logLik Deviance AIC BIC AICc
## -178.5072 357.0145 375.0145 399.8740 376.6967
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5855 0.7652 6 no Study_ID no
## sigma^2.2 0.0000 0.0001 19 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 19 no Species_common no
## sigma^2.4 0.5427 0.7367 15 no PFAS_type no
## sigma^2.5 0.9342 0.9666 121 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 117) = 4133.3711, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.2697, p-val = 0.0067
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.3203 0.4139 -3.1901 117 0.0018
## scale(Length_cooking_time_in_s) -0.3688 0.1577 -2.3388 117 0.0210
## scale(PFAS_carbon_chain) -0.0493 0.1813 -0.2719 117 0.7862
## scale(log(Ratio_liquid_fish_0 + 1)) -0.6386 0.2446 -2.6109 117 0.0102
## ci.lb ci.ub
## intrcpt -2.1400 -0.5007 **
## scale(Length_cooking_time_in_s) -0.6810 -0.0565 *
## scale(PFAS_carbon_chain) -0.4083 0.3097
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1230 -0.1542 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_water0)## R2_marginal R2_coditional
## 0.2094285 0.6418820Comparison between steaming and other water-based cooking categories
In our data set, the studies using steaming-based cooking were considered to have an unknown (i.e. NA) because of the difficulty to assess how much liquid gets in contact with the products. Here, we provide an analysis to compare steaming with other water-based cooking categories
water_dat$steamed<-ifelse(water_dat$Cooking_method=="Steaming","steamed","other") # create a dummy variable to differentiate "steaming" with other types of water-based cooking
full_model_water_steamed <- run_model_water(water_dat, ~ -1 + # without intercept
steamed +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain)) # In this case, we need to remove the Ratio liquid fish from the model. Otherwise, it would remove observations where the liquid volume was unknown.
summary(full_model_water_steamed)##
## Multivariate Meta-Analysis Model (k = 140; method: REML)
##
## logLik Deviance AIC BIC AICc
## -210.4341 420.8682 438.8682 465.0821 440.2968
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6927 0.8323 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 23 no Phylogeny yes
## sigma^2.3 0.0580 0.2409 23 no Species_common no
## sigma^2.4 0.2654 0.5151 15 no PFAS_type no
## sigma^2.5 0.9774 0.9886 140 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 136) = 4661.5782, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 136) = 1.8805, p-val = 0.1174
##
## Model Results:
##
## estimate se tval df pval
## steamedother -0.7212 0.3858 -1.8691 136 0.0638
## steamedsteamed -0.5578 0.4489 -1.2425 136 0.2162
## scale(Length_cooking_time_in_s) -0.3071 0.1590 -1.9316 136 0.0555
## scale(PFAS_carbon_chain) -0.0526 0.1409 -0.3731 136 0.7097
## ci.lb ci.ub
## steamedother -1.4842 0.0418 .
## steamedsteamed -1.4455 0.3299
## scale(Length_cooking_time_in_s) -0.6216 0.0073 .
## scale(PFAS_carbon_chain) -0.3312 0.2261
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Contrast between steamed and non-steamed
full_model_water_steamed_cont <- run_model_water(water_dat,
~ steamed + # with intercept
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain))
summary(full_model_water_steamed_cont)##
## Multivariate Meta-Analysis Model (k = 140; method: REML)
##
## logLik Deviance AIC BIC AICc
## -210.4341 420.8682 438.8682 465.0821 440.2968
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6927 0.8323 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 23 no Phylogeny yes
## sigma^2.3 0.0580 0.2409 23 no Species_common no
## sigma^2.4 0.2654 0.5151 15 no PFAS_type no
## sigma^2.5 0.9774 0.9886 140 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 136) = 4661.5782, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 136) = 1.3549, p-val = 0.2593
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.7212 0.3858 -1.8691 136 0.0638
## steamedsteamed 0.1634 0.4029 0.4056 136 0.6856
## scale(Length_cooking_time_in_s) -0.3071 0.1590 -1.9316 136 0.0555
## scale(PFAS_carbon_chain) -0.0526 0.1409 -0.3731 136 0.7097
## ci.lb ci.ub
## intrcpt -1.4842 0.0418 .
## steamedsteamed -0.6333 0.9602
## scale(Length_cooking_time_in_s) -0.6216 0.0073 .
## scale(PFAS_carbon_chain) -0.3312 0.2261
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1save(full_model_water, full_model_water_steamed, full_model_water_steamed_cont, file = here("Rdata", "full_model_water.RData"))Dry cooking
Not very relevant because all effect sizes are from one study here. Also, the model does not converge when using VCV_lnRR
dry_dat<-filter(dat, Cooking_Category=="No liquid")
include <- row.names(cor_tree) %in% dry_dat$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_dry <- cor_tree[include, include] # Only include the species that match the reduced data set
run_model_dry<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
rma.mv(lnRR, var_lnRR, # run the model with var_lnRR instead of VCV
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_dry), # cor_tree_dry here
test = "t",
data = data)
}Full model
full_model_dry <- run_model_dry(dry_dat, ~scale(Length_cooking_time_in_s)) # Model does not converge with VCV_lnRR
summary(full_model_dry)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.7496 19.4991 31.4991 42.3391 33.7096
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0000 0.0000 1 yes Study_ID no
## sigma^2.2 0.0038 0.0614 8 no Phylogeny yes
## sigma^2.3 0.0125 0.1118 8 no Species_common no
## sigma^2.4 0.0767 0.2769 2 no PFAS_type no
## sigma^2.5 0.0000 0.0000 47 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 68.7069, p-val = 0.0130
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 64.9941, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.7776 0.2098 -3.7073 45 0.0006 -1.2001
## scale(Length_cooking_time_in_s) -0.3450 0.0428 -8.0619 45 <.0001 -0.4311
## ci.ub
## intrcpt -0.3551 ***
## scale(Length_cooking_time_in_s) -0.2588 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_dry)## R2_marginal R2_coditional
## 0.5614935 1.0000000save(full_model_dry, file = here("Rdata", "full_model_dry.RData"))Plots
Cooking time
Generate predictions
oil_dat <- filter(dat, Cooking_Category=="oil-based")
water_dat <- filter(dat, Cooking_Category=="water-based")
dry_dat <- filter(dat, Cooking_Category=="No liquid")
oil_dat_time<-filter(oil_dat, Length_cooking_time_in_s!="NA")
water_dat_time<-filter(water_dat, Length_cooking_time_in_s!="NA")
dry_dat_time<-filter(dry_dat, Length_cooking_time_in_s!="NA")
model_oil_time<-run_model_oil(oil_dat_time, ~Length_cooking_time_in_s)
model_water_time<-run_model_water(water_dat_time, ~Length_cooking_time_in_s)
model_dry_time<-run_model_dry(dry_dat_time, ~Length_cooking_time_in_s)
pred_oil_time<-predict.rma(model_oil_time)
pred_water_time<-predict.rma(model_water_time)
pred_dry_time<-predict.rma(model_dry_time)
oil_dat_time<-mutate(oil_dat_time,
ci.lb = pred_oil_time$ci.lb, # lower bound of the confidence interval for oil
ci.ub = pred_oil_time$ci.ub, # upper bound of the confidence interval for oil
fit = pred_oil_time$pred) # regression line for oil
water_dat_time<-mutate(water_dat_time,
ci.lb = pred_water_time$ci.lb, # lower bound of the confidence interval for water
ci.ub = pred_water_time$ci.ub, # upper bound of the confidence interval for water
fit = pred_water_time$pred) # regression line for water
dry_dat_time<-mutate(dry_dat_time,
ci.lb = pred_dry_time$ci.lb, # lower bound of the confidence interval for dry
ci.ub = pred_dry_time$ci.ub, # upper bound of the confidence interval for dry
fit = pred_dry_time$pred) # regression line for dryPlot
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=water_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=water_dat_time,aes(y = fit), size = 1.5, col="dodgerblue")+
geom_ribbon(data=oil_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=oil_dat_time,aes(y = fit), size = 1.5, col="goldenrod")+
geom_ribbon(data=dry_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=dry_dat_time,aes(y = fit), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category
##### Oil based
full_model_oil_time<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Predictions with the full model, with Ratio_liquid_fish taken as 0 for the dry cooking category
##### Oil based
full_model_oil_time0<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time0<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Liquid volume to tissue sample ratio
Single moderator models
oil_dat_vol <- filter(oil_dat, Ratio_liquid_fish != "NA")
water_dat_vol <- filter(water_dat, Ratio_liquid_fish != "NA")
model_oil_vol <- run_model_oil(oil_dat_vol, ~log(Ratio_liquid_fish))
model_water_vol <- run_model_water(water_dat_vol, ~log(Ratio_liquid_fish))
pred_oil_vol <- predict.rma(model_oil_vol)
pred_water_vol <- predict.rma(model_water_vol)
oil_dat_vol <- mutate(oil_dat_vol, ci.lb = pred_oil_vol$ci.lb, ci.ub = pred_oil_vol$ci.ub,
fit = pred_oil_vol$pred)
water_dat_vol <- mutate(water_dat_vol, ci.lb = pred_water_vol$ci.lb, ci.ub = pred_water_vol$ci.ub,
fit = pred_water_vol$pred)
oil_dat$log_Ratio_liquid_fish <- log(oil_dat$Ratio_liquid_fish)
water_dat$log_Ratio_liquid_fish <- log(water_dat$Ratio_liquid_fish)Plot
ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_vol, aes(y = fit), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = oil_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = oil_dat_vol, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue ratio (mL/g))",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))
Predictions with the full model
##### Oil based
full_model_oil_vol <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
0, oil_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol <- run_model_water(water_dat, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)
ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio (mL/g))",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full model
PFAS carbon chain length
Generate predictions with single moderator models
oil_dat_PFAS <- filter(oil_dat, PFAS_carbon_chain != "NA")
water_dat_PFAS <- filter(water_dat, PFAS_carbon_chain != "NA")
dry_dat_PFAS <- filter(dry_dat, PFAS_carbon_chain != "NA")
model_oil_PFAS <- run_model_oil(oil_dat_PFAS, ~PFAS_carbon_chain)
model_water_PFAS <- run_model_water(water_dat_PFAS, ~PFAS_carbon_chain)
model_dry_PFAS <- run_model_dry(dry_dat_PFAS, ~PFAS_carbon_chain)
pred_oil_PFAS <- predict.rma(model_oil_PFAS)
pred_water_PFAS <- predict.rma(model_water_PFAS)
pred_dry_PFAS <- predict.rma(model_dry_PFAS)
oil_dat_PFAS <- mutate(oil_dat_PFAS, ci.lb = pred_oil_PFAS$ci.lb, ci.ub = pred_oil_PFAS$ci.ub,
fit = pred_oil_PFAS$pred)
water_dat_PFAS <- mutate(water_dat_PFAS, ci.lb = pred_water_PFAS$ci.lb, ci.ub = pred_water_PFAS$ci.ub,
fit = pred_water_PFAS$pred)
dry_dat_PFAS <- mutate(dry_dat_PFAS, ci.lb = pred_dry_PFAS$ci.lb, ci.ub = pred_dry_PFAS$ci.ub,
fit = pred_dry_PFAS$pred)Plot
For some reason the plot doesn’t want to knit, although the script works
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + ggplot(dat,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + aes(x
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + PFAS_carbon_chain,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + y
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + lnRR,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + fill
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + Cooking_Category))
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + +
geom_ribbon(data = dry_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data = dry_dat_PFAS, aes(y = fit), size = 1.5, col = "palegreen3") +
geom_ribbon(data = water_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_PFAS, aes(y = fit), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = oil_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data = oil_dat_PFAS, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category
##### Oil based
full_model_oil_PFAS<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category
##### Oil based
full_model_oil_PFAS0<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS0<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish + 1)))
pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Publication bias
Funnel plot
funnel(full_model, yaxis = "seinv")
funnel(full_model)
Egger regressions
Full model with Ratio_liquid_fish taken as NA for the dry cooking category
egger_all <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(egger_all)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -423.1839 846.3678 872.3678 923.4525 873.3733
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0238 0.1543 7 no Study_ID no
## sigma^2.2 0.0000 0.0010 26 no Phylogeny yes
## sigma^2.3 0.1732 0.4161 26 no Species_common no
## sigma^2.4 0.1239 0.3520 17 no PFAS_type no
## sigma^2.5 0.4092 0.6397 384 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 376) = 4942.6818, p-val < .0001
##
## Test of Moderators (coefficients 1:8):
## F(df1 = 8, df2 = 376) = 13.8754, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_Categoryoil-based -0.6366 0.3663 -1.7380 376 0.0830
## Cooking_Categorywater-based -0.7885 0.3655 -2.1574 376 0.0316
## I(sqrt(1/N_tilde)) 0.0380 0.5939 0.0639 376 0.9491
## scale(Publication_year) 0.4304 0.0903 4.7662 376 <.0001
## scale(Temperature_in_Celsius) -0.3562 0.1231 -2.8931 376 0.0040
## scale(Length_cooking_time_in_s) -0.3340 0.0533 -6.2618 376 <.0001
## scale(PFAS_carbon_chain) 0.0813 0.0800 1.0168 376 0.3099
## scale(log(Ratio_liquid_fish)) -0.9229 0.1498 -6.1629 376 <.0001
## ci.lb ci.ub
## Cooking_Categoryoil-based -1.3568 0.0836 .
## Cooking_Categorywater-based -1.5072 -0.0699 *
## I(sqrt(1/N_tilde)) -1.1297 1.2057
## scale(Publication_year) 0.2528 0.6079 ***
## scale(Temperature_in_Celsius) -0.5983 -0.1141 **
## scale(Length_cooking_time_in_s) -0.4389 -0.2291 ***
## scale(PFAS_carbon_chain) -0.0759 0.2385
## scale(log(Ratio_liquid_fish)) -1.2174 -0.6285 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funnel(egger_all, yaxis = "seinv")
funnel(egger_all)
# funnel(egger_all, yaxis = 'seinv') little evidence
egger_n <- run_model(dat, ~I(sqrt(1/N_tilde)))
summary(egger_n)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -623.6127 1247.2255 1261.2255 1290.8664 1261.4486
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5953 0.7715 10 no Study_ID no
## sigma^2.2 0.0000 0.0004 38 no Phylogeny yes
## sigma^2.3 0.2191 0.4680 39 no Species_common no
## sigma^2.4 0.0973 0.3120 18 no PFAS_type no
## sigma^2.5 0.5011 0.7079 512 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 10646.5573, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.1075, p-val = 0.7431
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.2340 0.4017 -0.5824 510 0.5606 -1.0232 0.5553
## I(sqrt(1/N_tilde)) -0.1995 0.6083 -0.3279 510 0.7431 -1.3946 0.9956
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Full model with Ratio_liquid_fish taken as NA for the dry cooking category
egger_all0 <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(egger_all0)##
## Multivariate Meta-Analysis Model (k = 431; method: REML)
##
## logLik Deviance AIC BIC AICc
## -449.8422 899.6843 927.6843 984.3144 928.7163
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1210 0.3479 7 no Study_ID no
## sigma^2.2 0.0000 0.0001 26 no Phylogeny yes
## sigma^2.3 0.1677 0.4095 26 no Species_common no
## sigma^2.4 0.1397 0.3738 17 no PFAS_type no
## sigma^2.5 0.3591 0.5992 431 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 422) = 5071.6337, p-val < .0001
##
## Test of Moderators (coefficients 1:9):
## F(df1 = 9, df2 = 422) = 11.0177, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_CategoryNo liquid -2.2926 0.4486 -5.1107 422 <.0001
## Cooking_Categoryoil-based -0.6732 0.3833 -1.7565 422 0.0797
## Cooking_Categorywater-based -0.4139 0.3942 -1.0501 422 0.2943
## I(sqrt(1/N_tilde)) -0.0426 0.5892 -0.0724 422 0.9423
## scale(Publication_year) 0.3575 0.1263 2.8302 422 0.0049
## scale(Temperature_in_Celsius) 0.0081 0.0966 0.0834 422 0.9335
## scale(Length_cooking_time_in_s) -0.3688 0.0490 -7.5336 422 <.0001
## scale(PFAS_carbon_chain) 0.0750 0.0818 0.9168 422 0.3598
## scale(log(Ratio_liquid_fish_0 + 1)) -0.8403 0.1354 -6.2068 422 <.0001
## ci.lb ci.ub
## Cooking_CategoryNo liquid -3.1743 -1.4108 ***
## Cooking_Categoryoil-based -1.4266 0.0801 .
## Cooking_Categorywater-based -1.1887 0.3608
## I(sqrt(1/N_tilde)) -1.2008 1.1155
## scale(Publication_year) 0.1092 0.6057 **
## scale(Temperature_in_Celsius) -0.1819 0.1980
## scale(Length_cooking_time_in_s) -0.4650 -0.2726 ***
## scale(PFAS_carbon_chain) -0.0858 0.2358
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1064 -0.5742 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funnel(egger_all0, yaxis = "seinv")
funnel(egger_all0)
# funnel(egger_all, yaxis = 'seinv') little evidence
save(egger_all, egger_all0, egger_n, file = here("Rdata", "egger_regressions.RData"))Publication year
pub_year <- run_model(dat, ~Publication_year)
summary(pub_year)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -622.1910 1244.3820 1258.3820 1288.0229 1258.6051
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5573 0.7465 10 no Study_ID no
## sigma^2.2 0.0026 0.0508 38 no Phylogeny yes
## sigma^2.3 0.2199 0.4689 39 no Species_common no
## sigma^2.4 0.0977 0.3126 18 no PFAS_type no
## sigma^2.5 0.5004 0.7074 512 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 11056.9127, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 1.2825, p-val = 0.2580
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -165.3375 145.7116 -1.1347 510 0.2570 -451.6063
## Publication_year 0.0818 0.0723 1.1325 510 0.2580 -0.0601
## ci.ub
## intrcpt 120.9312
## Publication_year 0.2238
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_continuous(dat, pub_year, dat$Publication_year, "Publication year")
##
Sensitivity analyses
Leave-one-out analyses
Here, we iteratively removed one study at the time and investigated how it affects the overall mean. Removing one of the study particularly modifies the estimate, but none of these models show a significant overall difference in PFAS concentration with cooking.
dat$Study_ID<-as.factor(dat$Study_ID)
dat<-as.data.frame(dat) # Only work with a dataframe
VCV_matrix<-list() # will need new VCV matrices because the sample size will be iteratively reduced
Leave1studyout<-list() # create a list that will host the results of each model
for(i in 1:length(levels(dat$Study_ID))){ # N models = N studies
VCV_matrix[[i]]<-make_VCV_matrix(dat[dat$Study_ID != levels(dat$Study_ID)[i], ], V="var_lnRR", cluster="Cohort_ID", obs="Effect_ID") # Create a new VCV matrix for each new model
Leave1studyout[[i]] <- rma.mv(yi = lnRR, V = VCV_matrix[[i]], # Same model structure as all the models we fitted
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = dat[dat$Study_ID != levels(dat$Study_ID)[i], ]) # Generate a new model for each new data (iterative removal of one study at a time)
}
# The output is a list so we need to summarise the coefficients of all the models performed
results.Leave1studyout<-as.data.frame(cbind(
sapply(Leave1studyout, function(x) summary(x)$beta), # extract the beta coefficient from all models
sapply(Leave1studyout, function(x) summary(x)$se), # extract the standard error from all models
sapply(Leave1studyout, function(x) summary(x)$zval), # extract the z value from all models
sapply(Leave1studyout, function(x) summary(x)$pval), # extract the p value from all models
sapply(Leave1studyout, function(x) summary(x)$ci.lb), # extract the lower confidence interval for all models
sapply(Leave1studyout, function(x) summary(x)$ci.ub))) # extract the upper confidence interval for all models
colnames(results.Leave1studyout)=c("Estimate", "SE", "zval", "pval", "ci.lb", "ci.ub") # change column names
kable(results.Leave1studyout)%>% kable_styling("striped", position="left") %>% scroll_box(width="100%", height="500px") # Table of the results from all models| Estimate | SE | zval | pval | ci.lb | ci.ub |
|---|---|---|---|---|---|
| -0.3358200 | 0.3051501 | -1.1005074 | 0.2716313 | -0.9353287 | 0.2636888 |
| -0.4080024 | 0.3076228 | -1.3263074 | 0.1853410 | -1.0123891 | 0.1963844 |
| -0.4142946 | 0.3406963 | -1.2160233 | 0.2247270 | -1.0841695 | 0.2555802 |
| 0.0222058 | 0.2673637 | 0.0830547 | 0.9338424 | -0.5031283 | 0.5475399 |
| -0.3372414 | 0.3106935 | -1.0854474 | 0.2782695 | -0.9477320 | 0.2732491 |
| -0.2492592 | 0.2988556 | -0.8340453 | 0.4046643 | -0.8364648 | 0.3379465 |
| -0.3419289 | 0.3089696 | -1.1066751 | 0.2689687 | -0.9489735 | 0.2651156 |
| -0.2278845 | 0.3065464 | -0.7433932 | 0.4577908 | -0.8309927 | 0.3752236 |
| -0.3927011 | 0.3175715 | -1.2365753 | 0.2168470 | -1.0166967 | 0.2312945 |
| -0.4892206 | 0.2852553 | -1.7150270 | 0.0870211 | -1.0498019 | 0.0713607 |
dat %>% group_by(Author_year, Study_ID) %>% summarise(mean=mean(lnRR)) # Study F005 (DelGobbo_2008) has much lower effect sizes than the others. ## # A tibble: 10 x 3
## # Groups: Author_year [10]
## Author_year Study_ID mean
## <chr> <fct> <dbl>
## 1 Alves_2017 F001 -0.0774
## 2 Barbosa_2018 F002 0.198
## 3 Bhavsar_2014 F003 0.153
## 4 DelGobbo_2008 F005 -2.00
## 5 Hu_2020 F006 -0.134
## 6 Kim_2020 F007 -0.887
## 7 Luo_2019 F008 -0.161
## 8 Sungur_2019 F010 -0.893
## 9 Taylor_2019 F011 0.208
## 10 Vassiliadou_2015 F013 0.671Subset analysis without Study_ID F005 (Del Gobbo et al. 2008)
Cooking time
dat.sens <- filter(dat, Author_year != "DelGobbo_2008")
include <- row.names(cor_tree) %in% dat.sens$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens <- cor_tree[include, include] # Only include the species that match the reduced data set
dat.sens <- as.data.frame(dat.sens) # convert data set into a data frame to calculate VCV matrix
VCV_lnRR.sens <- make_VCV_matrix(dat.sens, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens <- rma.mv(lnRR, VCV_lnRR.sens, mods = ~Length_cooking_time_in_s, random = list(~1 |
Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
R = list(Phylogeny = cor_tree_sens), test = "t", data = dat.sens)
summary(mod.sens)##
## Multivariate Meta-Analysis Model (k = 430; method: REML)
##
## logLik Deviance AIC BIC AICc
## -261.7947 523.5893 537.5893 566.0032 537.8560
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1970 0.4439 8 no Study_ID no
## sigma^2.2 0.0194 0.1391 22 no Phylogeny yes
## sigma^2.3 0.0159 0.1262 22 no Species_common no
## sigma^2.4 0.0948 0.3079 17 no PFAS_type no
## sigma^2.5 0.0882 0.2970 430 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 428) = 2086.8907, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 428) = 105.5222, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.6221 0.2188 2.8434 428 0.0047 0.1920
## Length_cooking_time_in_s -0.0012 0.0001 -10.2724 428 <.0001 -0.0014
## ci.ub
## intrcpt 1.0521 **
## Length_cooking_time_in_s -0.0009 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1dat.time.sens <- filter(dat.sens, Length_cooking_time_in_s != "NA")
plot_continuous(dat.time.sens, mod.sens, dat.time.sens$Length_cooking_time_in_s,
"Cooking time (s)") # The relationship with cooking time appears even stronger
Effect of cooking time on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")
oil_dat_time.sens <- filter(oil_dat.sens, Length_cooking_time_in_s != "NA")
water_dat_time.sens <- filter(water_dat.sens, Length_cooking_time_in_s != "NA")
dry_dat_time.sens <- filter(dry_dat.sens, Length_cooking_time_in_s != "NA")
model_oil_time.sens <- run_model_oil(oil_dat_time.sens, ~Length_cooking_time_in_s)
model_water_time.sens <- run_model_water(water_dat_time.sens, ~Length_cooking_time_in_s)
model_dry_time.sens <- run_model_dry(dry_dat_time.sens, ~Length_cooking_time_in_s)
summary(model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -123.3924 246.7848 260.7848 285.7364 261.2275
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2748 0.5242 5 no Study_ID no
## sigma^2.2 0.0000 0.0001 15 no Phylogeny yes
## sigma^2.3 0.0151 0.1230 15 no Species_common no
## sigma^2.4 0.1430 0.3781 16 no PFAS_type no
## sigma^2.5 0.0393 0.1982 263 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 261) = 750.9246, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 261) = 99.3161, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4430 0.2800 1.5817 261 0.1149 -0.1085
## Length_cooking_time_in_s -0.0015 0.0002 -9.9657 261 <.0001 -0.0018
## ci.ub
## intrcpt 0.9944
## Length_cooking_time_in_s -0.0012 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 120; method: REML)
##
## logLik Deviance AIC BIC AICc
## -100.6968 201.3935 215.3935 234.7883 216.4117
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1337 0.3656 7 no Study_ID no
## sigma^2.2 0.0076 0.0871 17 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 17 no Species_common no
## sigma^2.4 0.0960 0.3098 15 no PFAS_type no
## sigma^2.5 0.1786 0.4226 120 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 118) = 1103.1579, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 118) = 21.9447, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.6521 0.2644 2.4663 118 0.0151 0.1285
## Length_cooking_time_in_s -0.0012 0.0002 -4.6845 118 <.0001 -0.0017
## ci.ub
## intrcpt 1.1756 *
## Length_cooking_time_in_s -0.0007 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_dry_time.sens)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.7496 19.4991 31.4991 42.3391 33.7096
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0000 0.0000 1 yes Study_ID no
## sigma^2.2 0.0038 0.0614 8 no Phylogeny yes
## sigma^2.3 0.0125 0.1118 8 no Species_common no
## sigma^2.4 0.0767 0.2769 2 no PFAS_type no
## sigma^2.5 0.0000 0.0000 47 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 68.7069, p-val = 0.0130
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 64.9941, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4745 0.2610 1.8182 45 0.0757 -0.0511
## Length_cooking_time_in_s -0.0014 0.0002 -8.0619 45 <.0001 -0.0018
## ci.ub
## intrcpt 1.0001 .
## Length_cooking_time_in_s -0.0011 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_time.sens <- predict.rma(model_oil_time.sens)
pred_water_time.sens <- predict.rma(model_water_time.sens)
pred_dry_time.sens <- predict.rma(model_dry_time.sens)
oil_dat_time.sens <- mutate(oil_dat_time.sens, ci.lb = pred_oil_time.sens$ci.lb,
ci.ub = pred_oil_time.sens$ci.ub, fit = pred_oil_time.sens$pred)
water_dat_time.sens <- mutate(water_dat_time.sens, ci.lb = pred_water_time.sens$ci.lb,
ci.ub = pred_water_time.sens$ci.ub, fit = pred_water_time.sens$pred)
dry_dat_time.sens <- mutate(dry_dat_time.sens, ci.lb = pred_dry_time.sens$ci.lb,
ci.ub = pred_dry_time.sens$ci.ub, fit = pred_dry_time.sens$pred)For some reason the plot doesn’t want to knit, although the script works
# Actual plot
ggplot(dat.sens, aes(x = Length_cooking_time_in_s, y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_time.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = oil_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_time.sens, aes(y = fit), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_ribbon(data = dry_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.25) + geom_line(data = dry_dat_time.sens, aes(y = fit), size = 1.5,
col = "palegreen3") + col = "palegreen3") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "Cooking time (s)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))Predictions with the full model
##### Oil based
full_model_oil_time.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -103.6149 207.2299 227.2299 262.5242 228.1427
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1966 0.4434 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0179 0.1336 15 no Species_common no
## sigma^2.4 0.1114 0.3337 16 no PFAS_type no
## sigma^2.5 0.0287 0.1694 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4897 0.2498 1.9608 252 0.0510 -0.0021
## scale(Temperature_in_Celsius) -0.0679 0.1352 -0.5017 252 0.6163 -0.3342
## Length_cooking_time_in_s -0.0015 0.0001 -10.2481 252 <.0001 -0.0018
## scale(PFAS_carbon_chain) 0.1421 0.0730 1.9471 252 0.0526 -0.0016
## scale(log(Ratio_liquid_fish)) -0.1155 0.2578 -0.4481 252 0.6545 -0.6233
## ci.ub
## intrcpt 0.9816 .
## scale(Temperature_in_Celsius) 0.1985
## Length_cooking_time_in_s -0.0012 ***
## scale(PFAS_carbon_chain) 0.2858 .
## scale(log(Ratio_liquid_fish)) 0.3922
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_time.sens<-predict.rma(full_model_oil_time.sens, addx=TRUE, newmods=cbind(0,oil_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time.sens<-as.data.frame(pred_oil_time.sens)
pred_oil_time.sens$Length_cooking_time_in_s=pred_oil_time.sens$X.Length_cooking_time_in_s
pred_oil_time.sens<-left_join(oil_dat.sens, pred_oil_time.sens, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -59.3606 118.7212 136.7212 159.8936 138.7901
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1806 0.4250 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1187 0.3446 15 no PFAS_type no
## sigma^2.5 0.0659 0.2567 101 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 97) = 330.7425, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 15.8996, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.3223 0.2894 1.1134 97 0.2683 -0.2522
## Length_cooking_time_in_s -0.0013 0.0002 -6.1671 97 <.0001 -0.0018
## scale(PFAS_carbon_chain) 0.1680 0.0819 2.0524 97 0.0428 0.0055
## scale(log(Ratio_liquid_fish)) -0.3030 0.1596 -1.8984 97 0.0606 -0.6198
## ci.ub
## intrcpt 0.8967
## Length_cooking_time_in_s -0.0009 ***
## scale(PFAS_carbon_chain) 0.3306 *
## scale(log(Ratio_liquid_fish)) 0.0138 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_water_time.sens<-predict.rma(full_model_water_time.sens, addx=TRUE, newmods=cbind(water_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time.sens<-as.data.frame(pred_water_time.sens)
pred_water_time.sens$Length_cooking_time_in_s=pred_water_time.sens$X.Length_cooking_time_in_s
pred_water_time.sens<-left_join(water_dat, pred_water_time.sens, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time.sens<- run_model_dry(dry_dat.sens, ~ Length_cooking_time_in_s)
pred_dry_time.sens<-predict.rma(full_model_dry_time.sens, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time.sens<-as.data.frame(pred_dry_time.sens)
pred_dry_time.sens$Length_cooking_time_in_s=pred_dry_time.sens$X.Length_cooking_time_in_s
pred_dry_time.sens<-left_join(dry_dat.sens, pred_dry_time.sens, by="Length_cooking_time_in_s")
ggplot(dat.sens,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_time.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Liquid volume to tissue sample ratio
Ratio_liquid_fish taken as NA for the dry cooking category
dat.sens.vol <- filter(dat.sens, Ratio_liquid_fish != "NA")
include <- row.names(cor_tree) %in% dat.sens.vol$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens.vol <- cor_tree[include, include] # Only include the species that match the reduced data set
VCV_lnRR.sens.vol <- make_VCV_matrix(dat.sens.vol, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.vol <- rma.mv(lnRR, VCV_lnRR.sens.vol, mods = ~log(Ratio_liquid_fish), random = list(~1 |
Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
R = list(Phylogeny = cor_tree_sens.vol), test = "t", data = dat.sens.vol)
summary(mod.sens.vol)##
## Multivariate Meta-Analysis Model (k = 398; method: REML)
##
## logLik Deviance AIC BIC AICc
## -367.3849 734.7698 748.7698 776.6397 749.0584
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2778 0.5270 7 no Study_ID no
## sigma^2.2 0.0434 0.2083 26 no Phylogeny yes
## sigma^2.3 0.0929 0.3048 27 no Species_common no
## sigma^2.4 0.1221 0.3494 18 no PFAS_type no
## sigma^2.5 0.2322 0.4819 398 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 396) = 3756.6307, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 396) = 0.1389, p-val = 0.7096
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.1182 0.2678 -0.4415 396 0.6591 -0.6447 0.4083
## log(Ratio_liquid_fish) -0.0151 0.0404 -0.3726 396 0.7096 -0.0945 0.0644
##
## intrcpt
## log(Ratio_liquid_fish)
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_continuous(dat.sens.vol, mod.sens.vol, log(dat.sens.vol$Ratio_liquid_fish),
"ln(Liquid volume to tissue sample ratio (mL/g))") + scale_fill_manual(values = c("goldenrod2",
"dodgerblue3"))
Ratio_liquid_fish taken as 0 for the dry cooking category
dat.sens.vol0 <- filter(dat.sens, Ratio_liquid_fish_0 != "NA")
include <- row.names(cor_tree) %in% dat.sens.vol0$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens.vol0 <- cor_tree[include, include] # Only include the species that match the reduced data set
VCV_lnRR.sens.vol0 <- make_VCV_matrix(dat.sens.vol0, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.vol0 <- rma.mv(lnRR, VCV_lnRR.sens.vol0, mods = ~log(Ratio_liquid_fish_0 +
1), random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
~1 | Effect_ID), R = list(Phylogeny = cor_tree_sens.vol0), test = "t", data = dat.sens.vol0)
summary(mod.sens.vol0)##
## Multivariate Meta-Analysis Model (k = 467; method: REML)
##
## logLik Deviance AIC BIC AICc
## -424.5985 849.1970 863.1970 892.1913 863.4421
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2773 0.5266 7 no Study_ID no
## sigma^2.2 0.0431 0.2076 26 no Phylogeny yes
## sigma^2.3 0.1304 0.3611 27 no Species_common no
## sigma^2.4 0.1279 0.3576 18 no PFAS_type no
## sigma^2.5 0.2269 0.4764 467 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 465) = 5227.9857, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 465) = 2.5539, p-val = 0.1107
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.0411 0.2740 -0.1500 465 0.8808 -0.5796
## log(Ratio_liquid_fish_0 + 1) -0.0448 0.0280 -1.5981 465 0.1107 -0.0999
## ci.ub
## intrcpt 0.4974
## log(Ratio_liquid_fish_0 + 1) 0.0103
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_continuous(dat.sens.vol0, mod.sens.vol0, log(dat.sens.vol0$Ratio_liquid_fish_0 +
1), "ln(Liquid volume to tissue sample ratio + 1) (mL/g)") + scale_fill_manual(values = c("#55C667FF",
"goldenrod2", "dodgerblue3"))
Effect of cooking time on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
oil_dat_vol.sens <- filter(oil_dat.sens, Ratio_liquid_fish != "NA")
water_dat_vol.sens <- filter(water_dat.sens, Ratio_liquid_fish != "NA")
model_oil_vol.sens <- run_model_oil(oil_dat_vol.sens, ~log(Ratio_liquid_fish))
model_water_vol.sens <- run_model_water(water_dat_vol.sens, ~log(Ratio_liquid_fish))
summary(model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 297; method: REML)
##
## logLik Deviance AIC BIC AICc
## -288.4232 576.8464 590.8464 616.6553 591.2367
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4059 0.6371 6 no Study_ID no
## sigma^2.2 0.0646 0.2541 23 no Phylogeny yes
## sigma^2.3 0.0792 0.2815 24 no Species_common no
## sigma^2.4 0.0837 0.2893 17 no PFAS_type no
## sigma^2.5 0.2703 0.5199 297 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 295) = 3239.8130, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 295) = 0.0004, p-val = 0.9831
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.0787 0.3330 -0.2365 295 0.8132 -0.7341 0.5766
## log(Ratio_liquid_fish) 0.0010 0.0465 0.0212 295 0.9831 -0.0905 0.0925
##
## intrcpt
## log(Ratio_liquid_fish)
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_water_vol.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -78.6093 157.2186 171.2186 189.3844 172.4494
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5489 0.7409 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1371 0.3703 15 no PFAS_type no
## sigma^2.5 0.1173 0.3424 101 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 99) = 501.2657, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 99) = 6.6516, p-val = 0.0114
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.4829 0.4930 0.9796 99 0.3297 -0.4952 1.4611
## log(Ratio_liquid_fish) -0.4474 0.1735 -2.5791 99 0.0114 -0.7917 -0.1032
##
## intrcpt
## log(Ratio_liquid_fish) *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_vol.sens <- predict.rma(model_oil_vol.sens)
pred_water_vol.sens <- predict.rma(model_water_vol.sens)
oil_dat_vol.sens <- mutate(oil_dat_vol.sens, ci.lb = pred_oil_vol.sens$ci.lb, ci.ub = pred_oil_vol.sens$ci.ub,
fit = pred_oil_vol.sens$pred)
water_dat_vol.sens <- mutate(water_dat_vol.sens, ci.lb = pred_water_vol.sens$ci.lb,
ci.ub = pred_water_vol.sens$ci.ub, fit = pred_water_vol.sens$pred)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_vol.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = oil_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_vol.sens, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))Predictions with the full model
##### Oil based
full_model_oil_vol.sens <- run_model_oil(oil_dat.sens, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
summary(full_model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -103.6149 207.2299 227.2299 262.5242 228.1427
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1966 0.4434 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0179 0.1336 15 no Species_common no
## sigma^2.4 0.1114 0.3337 16 no PFAS_type no
## sigma^2.5 0.0287 0.1694 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.6350 0.2419 -2.6251 252 0.0092
## scale(Temperature_in_Celsius) -0.0679 0.1352 -0.5017 252 0.6163
## scale(Length_cooking_time_in_s) -0.3947 0.0385 -10.2481 252 <.0001
## scale(PFAS_carbon_chain) 0.1421 0.0730 1.9471 252 0.0526
## log_Ratio_liquid_fish -0.0354 0.0790 -0.4481 252 0.6545
## ci.lb ci.ub
## intrcpt -1.1114 -0.1586 **
## scale(Temperature_in_Celsius) -0.3342 0.1985
## scale(Length_cooking_time_in_s) -0.4706 -0.3189 ***
## scale(PFAS_carbon_chain) -0.0016 0.2858 .
## log_Ratio_liquid_fish -0.1909 0.1201
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_vol.sens <- predict.rma(full_model_oil_vol.sens, addx = TRUE, newmods = cbind(0,
0, 0, oil_dat.sens$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol.sens <- as.data.frame(pred_oil_vol.sens)
pred_oil_vol.sens <- pred_oil_vol.sens %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol.sens <- run_model_water(water_dat.sens, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
summary(full_model_water_vol.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -59.3606 118.7212 136.7212 159.8936 138.7901
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1806 0.4250 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1187 0.3446 15 no PFAS_type no
## sigma^2.5 0.0659 0.2567 101 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 97) = 330.7425, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 15.8996, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.0415 0.3623 0.1145 97 0.9091 -0.6777
## scale(Length_cooking_time_in_s) -0.4700 0.0762 -6.1671 97 <.0001 -0.6213
## scale(PFAS_carbon_chain) 0.1680 0.0819 2.0524 97 0.0428 0.0055
## log_Ratio_liquid_fish -0.2639 0.1390 -1.8984 97 0.0606 -0.5397
## ci.ub
## intrcpt 0.7607
## scale(Length_cooking_time_in_s) -0.3187 ***
## scale(PFAS_carbon_chain) 0.3306 *
## log_Ratio_liquid_fish 0.0120 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_water_vol.sens <- predict.rma(full_model_water_vol.sens, addx = TRUE, newmods = cbind(0,
0, water_dat.sens$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol.sens <- as.data.frame(pred_water_vol.sens)
pred_water_vol.sens <- pred_water_vol.sens %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol.sens, aes(y = pred), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = pred_oil_vol.sens, aes(y = pred), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2)) #### The line doesn't go all the way down (the predict function doesn't capture the biggest values)PFAS carbon chain
dat.sens.PFAS <- filter(dat.sens, PFAS_carbon_chain != "NA")
include <- row.names(cor_tree) %in% dat.sens.PFAS$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens.PFAS <- cor_tree[include, include] # Only include the species that match the reduced data set
VCV_lnRR.sens.PFAS <- make_VCV_matrix(dat.sens.PFAS, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.PFAS <- rma.mv(lnRR, VCV_lnRR.sens.PFAS, mods = ~PFAS_carbon_chain, random = list(~1 |
Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
R = list(Phylogeny = cor_tree_sens.PFAS), test = "t", data = dat.sens.PFAS)
summary(mod.sens.PFAS)##
## Multivariate Meta-Analysis Model (k = 486; method: REML)
##
## logLik Deviance AIC BIC AICc
## -451.3336 902.6673 916.6673 945.9419 916.9026
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2362 0.4860 9 no Study_ID no
## sigma^2.2 0.0932 0.3054 30 no Phylogeny yes
## sigma^2.3 0.1079 0.3284 31 no Species_common no
## sigma^2.4 0.0902 0.3003 18 no PFAS_type no
## sigma^2.5 0.2440 0.4939 486 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 484) = 6303.2923, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 484) = 1.3174, p-val = 0.2516
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.2521 0.3602 -0.6999 484 0.4844 -0.9599 0.4557
## PFAS_carbon_chain 0.0307 0.0268 1.1478 484 0.2516 -0.0219 0.0834
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_continuous(dat.sens.PFAS, mod.sens.PFAS, dat.sens.PFAS$PFAS_carbon_chain, "PFAS carbon chain length") # The relationship with cooking time appears even stronger
Effect of carbon chain length on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")
oil_dat_PFAS.sens <- filter(oil_dat.sens, PFAS_carbon_chain != "NA")
water_dat_PFAS.sens <- filter(water_dat.sens, PFAS_carbon_chain != "NA")
dry_dat_PFAS.sens <- filter(dry_dat.sens, PFAS_carbon_chain != "NA")
model_oil_PFAS.sens <- run_model_oil(oil_dat_PFAS.sens, ~PFAS_carbon_chain)
model_water_PFAS.sens <- run_model_water(water_dat_PFAS.sens, ~PFAS_carbon_chain)
model_dry_PFAS.sens <- run_model_dry(dry_dat_PFAS.sens, ~PFAS_carbon_chain)
summary(model_oil_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 297; method: REML)
##
## logLik Deviance AIC BIC AICc
## -288.4769 576.9538 590.9538 616.7626 591.3440
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.3725 0.6103 6 no Study_ID no
## sigma^2.2 0.0727 0.2697 23 no Phylogeny yes
## sigma^2.3 0.0773 0.2780 24 no Species_common no
## sigma^2.4 0.0747 0.2733 17 no PFAS_type no
## sigma^2.5 0.2709 0.5205 297 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 295) = 3551.6546, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 295) = 1.4816, p-val = 0.2245
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.3791 0.4123 -0.9196 295 0.3585 -1.1905 0.4322
## PFAS_carbon_chain 0.0344 0.0283 1.2172 295 0.2245 -0.0212 0.0900
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_water_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 120; method: REML)
##
## logLik Deviance AIC BIC AICc
## -109.5717 219.1435 233.1435 252.5383 234.1617
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1362 0.3691 7 no Study_ID no
## sigma^2.2 0.0699 0.2643 17 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 17 no Species_common no
## sigma^2.4 0.0706 0.2657 15 no PFAS_type no
## sigma^2.5 0.2176 0.4665 120 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 118) = 1199.5376, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 118) = 2.0593, p-val = 0.1539
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.5743 0.3686 -1.5583 118 0.1218 -1.3042 0.1555
## PFAS_carbon_chain 0.0480 0.0334 1.4350 118 0.1539 -0.0182 0.1141
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_dry_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 69; method: REML)
##
## logLik Deviance AIC BIC AICc
## -67.7369 135.4737 149.4737 164.9066 151.3721
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4774 0.6909 2 no Study_ID no
## sigma^2.2 0.1980 0.4450 13 no Phylogeny yes
## sigma^2.3 0.0483 0.2198 14 no Species_common no
## sigma^2.4 0.0170 0.1305 7 no PFAS_type no
## sigma^2.5 0.2878 0.5364 69 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 67) = 1192.4310, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 67) = 5.2409, p-val = 0.0252
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -1.1648 0.8497 -1.3708 67 0.1750 -2.8607 0.5312
## PFAS_carbon_chain 0.1611 0.0704 2.2893 67 0.0252 0.0206 0.3016 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_PFAS.sens <- predict.rma(model_oil_PFAS.sens)
pred_water_PFAS.sens <- predict.rma(model_water_PFAS.sens)
pred_dry_PFAS.sens <- predict.rma(model_dry_PFAS.sens)
oil_dat_PFAS.sens <- mutate(oil_dat_PFAS.sens, ci.lb = pred_oil_PFAS.sens$ci.lb,
ci.ub = pred_oil_PFAS.sens$ci.ub, fit = pred_oil_PFAS.sens$pred)
water_dat_PFAS.sens <- mutate(water_dat_PFAS.sens, ci.lb = pred_water_PFAS.sens$ci.lb,
ci.ub = pred_water_PFAS.sens$ci.ub, fit = pred_water_PFAS.sens$pred)
dry_dat_PFAS.sens <- mutate(dry_dat_PFAS.sens, ci.lb = pred_dry_PFAS.sens$ci.lb,
ci.ub = pred_dry_PFAS.sens$ci.ub, fit = pred_dry_PFAS.sens$pred)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = dry_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = dry_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "palegreen3") + col = "palegreen3") +
geom_ribbon(data = oil_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_ribbon(data = water_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = water_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))Predictions with the full model
##### Oil based
full_model_oil_PFAS.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -103.6149 207.2299 227.2299 262.5242 228.1427
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1966 0.4434 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0179 0.1336 15 no Species_common no
## sigma^2.4 0.1114 0.3337 16 no PFAS_type no
## sigma^2.5 0.0287 0.1694 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.1519 0.3612 -3.1889 252 0.0016
## scale(Temperature_in_Celsius) -0.0679 0.1352 -0.5017 252 0.6163
## scale(Length_cooking_time_in_s) -0.3947 0.0385 -10.2481 252 <.0001
## PFAS_carbon_chain 0.0575 0.0296 1.9471 252 0.0526
## scale(log(Ratio_liquid_fish)) -0.1155 0.2578 -0.4481 252 0.6545
## ci.lb ci.ub
## intrcpt -1.8633 -0.4405 **
## scale(Temperature_in_Celsius) -0.3342 0.1985
## scale(Length_cooking_time_in_s) -0.4706 -0.3189 ***
## PFAS_carbon_chain -0.0007 0.1157 .
## scale(log(Ratio_liquid_fish)) -0.6233 0.3922
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_PFAS.sens<-predict.rma(full_model_oil_PFAS.sens, addx=TRUE, newmods=cbind(0,0, oil_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS.sens<-as.data.frame(pred_oil_PFAS.sens)
pred_oil_PFAS.sens$PFAS_carbon_chain=pred_oil_PFAS.sens$X.PFAS_carbon_chain
pred_oil_PFAS.sens<-left_join(oil_dat.sens, pred_oil_PFAS.sens, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
summary(full_model_water_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -59.3606 118.7212 136.7212 159.8936 138.7901
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1806 0.4250 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1187 0.3446 15 no PFAS_type no
## sigma^2.5 0.0659 0.2567 101 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 97) = 330.7425, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 15.8996, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -1.2501 0.3896 -3.2083 97 0.0018 -2.0234
## scale(Length_cooking_time_in_s) -0.4700 0.0762 -6.1671 97 <.0001 -0.6213
## PFAS_carbon_chain 0.0742 0.0361 2.0524 97 0.0428 0.0024
## scale(log(Ratio_liquid_fish)) -0.3030 0.1596 -1.8984 97 0.0606 -0.6198
## ci.ub
## intrcpt -0.4768 **
## scale(Length_cooking_time_in_s) -0.3187 ***
## PFAS_carbon_chain 0.1459 *
## scale(log(Ratio_liquid_fish)) 0.0138 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_water_PFAS.sens<-predict.rma(full_model_water_PFAS.sens, addx=TRUE, newmods=cbind(0, water_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS.sens<-as.data.frame(pred_water_PFAS.sens)
pred_water_PFAS.sens$PFAS_carbon_chain=pred_water_PFAS.sens$X.PFAS_carbon_chain
pred_water_PFAS.sens<-left_join(water_dat.sens, pred_water_PFAS.sens, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS.sens<- run_model_dry(dry_dat.sens, ~ PFAS_carbon_chain)
pred_dry_PFAS.sens<-predict.rma(full_model_dry_PFAS.sens, addx=TRUE)
pred_dry_PFAS.sens<-as.data.frame(pred_dry_PFAS.sens)
pred_dry_PFAS.sens$PFAS_carbon_chain=pred_dry_PFAS.sens$X.PFAS_carbon_chain
pred_dry_PFAS.sens<-left_join(dry_dat.sens, pred_dry_PFAS.sens, by="PFAS_carbon_chain")
ggplot(dat.sens,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Funnel plot
full_mod.sens <- run_model(dat.sens, ~-1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))
funnel(full_mod.sens, yaxis = "seinv")
Figures for publication
Figure 2
Cooking time
full_model_time<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_time<-predict.rma(full_model_time, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time<-as.data.frame(pred_full_model_time)
pred_full_model_time$Length_cooking_time_in_s=pred_full_model_time$X.Length_cooking_time_in_s
pred_full_model_time<-left_join(dat, pred_full_model_time, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Liquid volume to tissue sample ratio
full_model_vol<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_full_model_vol<-predict.rma(full_model_vol, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish))
pred_full_model_vol<-as.data.frame(pred_full_model_vol)
pred_full_model_vol$log_Ratio_liquid_fish=pred_full_model_vol$X.log_Ratio_liquid_fish
pred_full_model_vol<- pred_full_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
uni_model_vol<- run_model(dat, ~ log_Ratio_liquid_fish)
pred_uni_model_vol<-predict.rma(uni_model_vol, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol<-as.data.frame(pred_uni_model_vol)
pred_uni_model_vol$log_Ratio_liquid_fish=pred_uni_model_vol$X.log_Ratio_liquid_fish
pred_uni_model_vol<- pred_uni_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
p_vol<-ggplot(dat,aes(x = log_Ratio_liquid_fish, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid volume to tissue sample ratio) (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Cooking temperature
full_model_temp<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_temp<-predict.rma(full_model_temp, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp<-as.data.frame(pred_full_model_temp)
pred_full_model_temp$Temperature_in_Celsius=pred_full_model_temp$X.Temperature_in_Celsius
pred_full_model_temp<-left_join(dat, pred_full_model_temp, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))PFAS carbon chain length
full_model_PFAS<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_full_model_PFAS<-predict.rma(full_model_PFAS, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS<-as.data.frame(pred_full_model_PFAS)
pred_full_model_PFAS$PFAS_carbon_chain=pred_full_model_PFAS$X.PFAS_carbon_chain
pred_full_model_PFAS<-left_join(dat, pred_full_model_PFAS, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Combine plots and save
(p_time + p_vol)/(p_temp + p_PFAS) + plot_annotation(tag_levels = c("A", "B", "C",
"D"))
ggsave("fig/Fig_2.png", width = 15, height = 12, dpi = 1200)Figure 2, with Ratio_liquid_fish taken as 0 for the dry cooking method
Cooking time
full_model_time0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_time0<-predict.rma(full_model_time0, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time0<-as.data.frame(pred_full_model_time0)
pred_full_model_time0$Length_cooking_time_in_s=pred_full_model_time0$X.Length_cooking_time_in_s
pred_full_model_time0<-left_join(dat, pred_full_model_time0, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Liquid volume to tissue sample ratio
full_model_vol0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish0)
pred_full_model_vol0<-predict.rma(full_model_vol0, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish0))
pred_full_model_vol0<-as.data.frame(pred_full_model_vol0)
pred_full_model_vol0$log_Ratio_liquid_fish0=pred_full_model_vol0$X.log_Ratio_liquid_fish
pred_full_model_vol0<- pred_full_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0)-1, lnRR = 0)
uni_model_vol0<- run_model(dat, ~ log_Ratio_liquid_fish0)
pred_uni_model_vol0<-predict.rma(uni_model_vol0, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol0<-as.data.frame(pred_uni_model_vol0)
pred_uni_model_vol0$log_Ratio_liquid_fish0=pred_uni_model_vol0$X.log_Ratio_liquid_fish
pred_uni_model_vol0<- pred_uni_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0) -1, lnRR = 0)
p_vol0<-ggplot(dat,aes(x = log_Ratio_liquid_fish0, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol0,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid volume to tissue sample ratio + 1) (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Cooking temperature
full_model_temp0<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_temp0<-predict.rma(full_model_temp0, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp0<-as.data.frame(pred_full_model_temp0)
pred_full_model_temp0$Temperature_in_Celsius=pred_full_model_temp0$X.Temperature_in_Celsius
pred_full_model_temp0<-left_join(dat, pred_full_model_temp0, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))PFAS carbon chain length
full_model_PFAS0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_PFAS0<-predict.rma(full_model_PFAS0, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS0<-as.data.frame(pred_full_model_PFAS0)
pred_full_model_PFAS0$PFAS_carbon_chain=pred_full_model_PFAS0$X.PFAS_carbon_chain
pred_full_model_PFAS0<-left_join(dat, pred_full_model_PFAS0, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Combine plots and save
(p_time0 + p_vol0)/(p_temp0 + p_PFAS0) + plot_annotation(tag_levels = c("A", "B",
"C", "D"))
ggsave("fig/Fig_2_zero_ratio.png", width = 15, height = 12, dpi = 1200)Figure 3
Adapt orchard_plot function
my_orchard<-function (object, mod = "Int", xlab, N = "none",
alpha = 0.5, angle = 90, cb = FALSE, transfm = c("none",
"tanh"), condition.lab = "Condition")
{
transfm <- match.arg(transfm)
if (any(class(object) %in% c("rma.mv", "rma"))) {
if (mod != "Int") {
object <- mod_results(object, mod)
}
else {
object <- mod_results(object, mod = "Int")
}
}
mod_table <- object$mod_table
data <- object$data
data$moderator <- factor(data$moderator, levels = mod_table$name,
labels = mod_table$name)
data$scale <- (1/sqrt(data[, "vi"]))
legend <- "Precision (1/SE)"
if (any(N != "none")) {
data$scale <- N
legend <- "Sample Size (N)"
}
if (transfm == "tanh") {
cols <- sapply(mod_table, is.numeric)
mod_table[, cols] <- Zr_to_r(mod_table[, cols])
data$yi <- Zr_to_r(data$yi)
label <- xlab
}
else {
label <- xlab
}
mod_table$K <- as.vector(by(data, data[, "moderator"],
function(x) length(x[, "yi"])))
group_no <- length(unique(mod_table[, "name"]))
cbpl <- c("#55C667FF", "goldenrod2", "dodgerblue3") # change colors
if (names(mod_table)[2] == "condition") {
condition_no <- length(unique(mod_table[, "condition"]))
plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data,
ggplot2::aes(y = yi, x = moderator, size = scale,
color = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name,
ymin = lowerPR, ymax = upperPR), size = 0.75, # change size confidence intervals and swap CL with PR. Added whiskers
position = ggplot2::position_dodge2(width = 0.3)) +
ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate,
x = name, ymin = lowerCL, ymax = upperCL, shape = as.factor(condition), # swap CL with PR
fill = name), size = 1.6, stroke=2.2, width= 1.3, position = ggplot2::position_dodge2(width = 0.3)) + # change size point and prediction intervals
ggplot2::scale_shape_manual(values = 20 + (1:condition_no)) +
ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.position = c(0,
1), legend.justification = c(0, 1)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(y = label, x = "", size = legend) +
ggplot2::labs(shape = condition.lab) + ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
}
else {
plot <- ggplot2::ggplot(data = mod_table, ggplot2::aes(x = estimate,
y = name)) + ggbeeswarm::geom_quasirandom(data = data,
ggplot2::aes(x = yi, y = moderator, size = scale,
colour = moderator), groupOnX = FALSE, alpha = alpha) +
ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerPR,
xmax = upperPR), height = 0, show.legend = FALSE, # change error barrs
size = 0.75, alpha = 0.5) + ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerCL,
xmax = upperCL), height = 0.1, show.legend = FALSE,
size = 1.75) + ggplot2::geom_vline(xintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_point(ggplot2::aes(fill = name), size = 8, # change point size
shape = 21) + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.position = c(1,
0), legend.justification = c(1, 0)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(x = label, y = "", size = legend) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
}
if (cb == TRUE) {
plot <- plot + ggplot2::scale_fill_manual(values = cbpl) +
ggplot2::scale_colour_manual(values = cbpl)
}
return(plot)
}Run full models in original units
full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# full model with Ratio_liquid_fish taken as `0` for the dry cooking category
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)
# full model without the 'No liquid' data for figure 3B, when Ratio_liquid_fish
# is taken as `NA` for the dry cooking category
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)Models with Ratio_liquid_fish taken as NA for the dry cooking category
Figure 3A
Estimates at cooking times of 2, 10 and 25 min
time_mm <-marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm<-my_orchard(time_mm, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.13))+
guides(size=F)Figure 3B
Estimates at 0 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm <-marginal_means(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", at = list(log_Ratio_liquid_fish= c(-2.3, 2.3, 3.8)), by = "log_Ratio_liquid_fish")
p_volume_mm<-my_orchard(volume_mm, xlab = "lnRR", condition.lab = "ln (Liquid to sample ratio)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.13))+
guides(size=F)Figure 3C
Estimates at cooking times of 2, 10 and 25 min
time_mm_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_cat<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.12))Combine plots and save
((p_time_mm/p_volume_mm) | p_time_mm_cat) + plot_annotation(tag_levels = c("A", "B",
"C"))
ggsave("fig/Fig_3.png", width = 14, height = 10, dpi = 1200)Models with Ratio_liquid_fish taken as 0 for the dry cooking category
Figure 3A
Estimates at cooking times of 2, 10 and 25 min
time_mm0 <-marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm0<-my_orchard(time_mm0, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.25))+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 431),
parse = TRUE, hjust = "right", size = 3.5)Figure 3B
Estimates at 0.1 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm0 <-marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(log_Ratio_liquid_fish0= c(0, 2.4, 3.8)), by = "log_Ratio_liquid_fish0")
p_volume_mm0<-my_orchard(volume_mm0, xlab = "lnRR", condition.lab = "ln (Liquid to sample ratio + 1)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.13))+
guides(size=F)+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 431),
parse = TRUE, hjust = "right", size = 3.5)Figure 3C
Estimates at cooking times of 2, 10 and 25 min
In this case, water- and oil-based cooking must be separated from dry cooking to avoid extrapolations of the dry cooking effect sizes at the mean liquid ratio.
time_mm_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_wat_oil<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 0), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.position = "none")+
guides(shape=F, size=F)+
ylim(-6.1, 3)+
annotate("text", y = 1.9, x = (seq(1, 2, 1) +
0.3), label = paste("italic(k)==", c(263, 121)),
parse = TRUE, hjust = "right", size = 3.5)
time_mm_dry<-marginal_means(full_model_org_units_dry, data = dat, at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_dry<-my_orchard(time_mm_dry, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("#55C667FF"))+
scale_colour_manual(values = c("#55C667FF"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.position = c(0.02,0.14),
legend.margin=margin(1,1,1,1))+
guides(size=F)+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 47),
parse = TRUE, hjust = "right", size = 3.5)
p_time_mm_cat<-p_time_mm_wat_oil/p_time_mm_dry + plot_layout(heights=c(2,1))Combine plots and save
((p_time_mm0/p_volume_mm0) | p_time_mm_cat) + plot_annotation(tag_levels = c("A",
"B", "C"))
ggsave("fig/Fig_3_zero_ratio.png", width = 14, height = 11, dpi = 1200)Figure 4, with Ratio_liquid_fish taken as NA for the dry cooking category
Figure 4A
##### Oil based
full_model_oil_time<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)Figure 4B
##### Oil based
full_model_oil_vol <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
0, oil_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol <- run_model_water(water_dat, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)
p_4B <- ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full modelFigure 4C
full_model_oil_temp<- run_model_oil(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_temp<-predict.rma(full_model_oil_temp, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp<-as.data.frame(pred_oil_temp)
pred_oil_temp$Temperature_in_Celsius=pred_oil_temp$X.Temperature_in_Celsius
pred_oil_temp<-left_join(oil_dat, pred_oil_temp, by="Temperature_in_Celsius")
p_4C<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)Figure 4D
##### Oil based
full_model_oil_PFAS<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Combine and save
(p_4A + p_4B)/(p_4C + p_4D) + plot_annotation(tag_levels = c("A", "B", "C", "D"))
ggsave("fig/Fig_4.png", width = 15, height = 12, dpi = 1200)Figure 4, with Ratio_liquid_fish taken as 0 for the dry cooking category
Figure 4A
##### Oil based
full_model_oil_time0<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time0<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)Figure 4B
##### Oil based
full_model_oil_vol0 <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_oil_vol0 <- predict.rma(full_model_oil_vol0, addx = TRUE, newmods = cbind(0,
0, 0, oil_dat$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol0 <- as.data.frame(pred_oil_vol0)
pred_oil_vol0 <- pred_oil_vol0 %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol0 <- run_model_water(water_dat, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_water_vol0 <- predict.rma(full_model_water_vol0, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol0 <- as.data.frame(pred_water_vol0)
pred_water_vol0 <- pred_water_vol0 %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "water-based",
lnRR = 0)
p_4B0 <- ggplot(dat, aes(x = log(Ratio_liquid_fish_0 + 1), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol0, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = pred_oil_vol0, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio + 1) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2))Figure 4C
full_model_oil_temp0<- run_model_oil(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_temp0<-predict.rma(full_model_oil_temp0, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp0<-as.data.frame(pred_oil_temp0)
pred_oil_temp0$Temperature_in_Celsius=pred_oil_temp0$X.Temperature_in_Celsius
pred_oil_temp0<-left_join(oil_dat, pred_oil_temp0, by="Temperature_in_Celsius")
p_4C0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)Figure 4D
##### Oil based
full_model_oil_PFAS0<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS0<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Combine and save
(p_4A0 + p_4B0)/(p_4C0 + p_4D0) + plot_annotation(tag_levels = c("A", "B", "C", "D"))
ggsave("fig/Fig_4_zero_ratio.png", width = 15, height = 12, dpi = 1200)Figure 5, with Ratio_liquid_fish taken as NA for the dry cooking category
Figure 5A
dat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f <- funnel(full_model,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=1,
ylim=c(0.82,0.94),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)
p_5A <- recordPlot(plot_f)
invisible(dev.off())Figure 5B
full_model_egger <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_egger<-predict.rma(full_model_egger, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger<-as.data.frame(pred_egger)
pred_egger$SE_eff_N=pred_egger$X.I.sqrt.1.N_tilde..
pred_egger<- pred_egger %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)Figure 5C
full_model_pub <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_pub<-predict.rma(full_model_pub, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub<-as.data.frame(pred_pub)
pred_pub$Publication_year=pred_pub$X.Publication_year
pred_pub<-left_join(dat, pred_pub, by="Publication_year")
p_5C<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))Combine and save
(ggdraw(p_5A) + ggdraw(p_5B) + ggdraw(p_5C) + plot_annotation(tag_levels = "A"))
ggsave(here("fig/Fig_5BC.png"), width = 18, height = 7, dpi = 1200)Figure 5, with Ratio_liquid_fish taken as 0 for the dry cooking category
Figure 5A
dat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f0 <- funnel(full_model0,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=1,
ylim=c(0.82,0.94),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)
p_5A0 <- recordPlot(plot_f0)
invisible(dev.off())Figure 5B
full_model_egger0 <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions
pred_egger0<-predict.rma(full_model_egger0, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger0<-as.data.frame(pred_egger0)
pred_egger0$SE_eff_N=pred_egger0$X.I.sqrt.1.N_tilde..
pred_egger0<- pred_egger0 %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B0<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger0,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)Figure 5C
full_model_pub0 <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions
pred_pub0<-predict.rma(full_model_pub0, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub0<-as.data.frame(pred_pub0)
pred_pub0$Publication_year=pred_pub0$X.Publication_year
pred_pub0<-left_join(dat, pred_pub0, by="Publication_year")
p_5C0<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub0,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))Combine and save
(ggdraw(p_5A0) + ggdraw(p_5B0) + ggdraw(p_5C0) + plot_annotation(tag_levels = "A"))
ggsave(here("fig/Fig_5BC_zero_ratio.png"), width = 18, height = 7, dpi = 1200)Software and packages versions
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] cowplot_1.1.1 GGally_2.1.2 kableExtra_1.3.4
## [4] emmeans_1.6.2-9990002 patchwork_1.1.1 clubSandwich_0.5.3
## [7] ape_5.5 orchaRd_0.0.0.9000 metaAidR_0.0.0.9000
## [10] metafor_3.0-2 Matrix_1.3-4 here_1.0.1
## [13] googlesheets4_1.0.0 forcats_0.5.1 stringr_1.4.0
## [16] dplyr_1.0.7 purrr_0.3.4 readr_2.0.0
## [19] tidyr_1.1.3 tibble_3.1.3 ggplot2_3.3.5
## [22] tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.0-10 googledrive_2.0.0 ggbeeswarm_0.6.0 colorspace_2.0-2
## [5] ellipsis_0.3.2 rprojroot_2.0.2 estimability_1.3 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.1.0 fansi_0.5.0 mvtnorm_1.1-2
## [13] lubridate_1.7.10 mathjaxr_1.4-0 xml2_1.3.2 codetools_0.2-18
## [17] splines_4.1.0 knitr_1.34 jsonlite_1.7.2 broom_0.7.9
## [21] dbplyr_2.1.1 compiler_4.1.0 httr_1.4.2 backports_1.2.1
## [25] assertthat_0.2.1 gargle_1.2.0 cli_3.0.1 formatR_1.11
## [29] htmltools_0.5.1.1 tools_4.1.0 coda_0.19-4 gtable_0.3.0
## [33] glue_1.4.2 Rcpp_1.0.7 cellranger_1.1.0 jquerylib_0.1.4
## [37] vctrs_0.3.8 svglite_2.0.0 nlme_3.1-152 xfun_0.24
## [41] rvest_1.0.1 lifecycle_1.0.0 MASS_7.3-54 zoo_1.8-9
## [45] scales_1.1.1 hms_1.1.0 parallel_4.1.0 sandwich_3.0-1
## [49] RColorBrewer_1.1-2 yaml_2.2.1 sass_0.4.0 reshape_0.8.8
## [53] stringi_1.7.3 highr_0.9 rlang_0.4.11 pkgconfig_2.0.3
## [57] systemfonts_1.0.2 evaluate_0.14 lattice_0.20-44 labeling_0.4.2
## [61] tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1 bookdown_0.22
## [65] R6_2.5.1 generics_0.1.0 multcomp_1.4-17 DBI_1.1.1
## [69] pillar_1.6.2 haven_2.4.3 withr_2.4.2 survival_3.2-11
## [73] modelr_0.1.8 crayon_1.4.1 utf8_1.2.2 tzdb_0.1.2
## [77] rmarkdown_2.11 grid_4.1.0 readxl_1.3.1 rmdformats_1.0.2
## [81] reprex_2.0.1 digest_0.6.27 webshot_0.5.2 xtable_1.8-4
## [85] munsell_0.5.0 beeswarm_0.4.0 viridisLite_0.4.0 vipor_0.4.5
## [89] bslib_0.2.5.1